**Preface to "Energy Performance in Buildings and Quality of Life"**

Buildings allow several kinds of human activity: work, eat, sleep, play, etc., and they have a role in determining quality of life: ugly and uncomfortable buildings can be the worst place to live. Building physics and building energy performance (BEP) research focuses on a several building-related issues: building typologies (e.g., schools, dwellings, social housing, heritage, etc.); construction technologies (e.g., wall, roof, windows including in prefabricated buildings, wall buildings, etc.); energy consumption and cost; energy saving; energy monitoring; and, furthermore, architectural design and the impact of specific building techniques, including material life cycle assessment and the role of materials on BEP. In this book, we highlight the relationship between BEP and quality of life.

We adopt the phrase "quality of life" because as all-inclusive term covering everything that impacts on habitual life, especially in terms of comfort, thermal comfort, and IAQ and household smartness (smart building, smart monitoring, or smart metering, following UE Directive 844/2018), as well as the reduction of energy poverty and the impact of buildings on the environment and global warming. Although the above list provides examples of specific topics of interest, our aim is, more broadly, to collect papers discussing the role of BEP in improving quality of life.

> **Kristian Fabbri** *Editor*

## *Article* **Climate Change E**ff**ect on Building Performance: A Case Study in New York**

## **Kristian Fabbri 1,\*, Jacopo Gaspari <sup>1</sup> and Licia Felicioni <sup>2</sup>**


Received: 18 May 2020; Accepted: 16 June 2020; Published: 18 June 2020

**Abstract:** The evidences of the influence of climate change (CC) in most of the key sectors of human activities are frequently reported by the news and media with increasing concern. The building sector, and particularly energy use in the residential sector, represents a crucial field of investigation as demonstrated by specific scientific literature. The paper reports a study on building energy consumption and the related effect on indoor thermal comfort considering the impacts of the Intergovernmental Panel on Climate Change (IPCC) 2018 report about temperature increase projection. The research includes a case study in New York City, assuming three different scenarios. The outcomes evidence a decrease in energy demand for heating and an increase in energy demand for cooling, with a relevant shift due to the summer period temperature variations. The challenge of the last decades for sustainable design was to increase insulation for improving thermal behavior, highly reducing the energy demand during winter time, however, the projections over the next decades suggest that the summer regime will represent a future and major challenge in order to reduce overheating and ensure comfortable (or at least acceptable) living conditions inside buildings. The growing request of energy for cooling is generating increasing pressure on the supply system with peaks in the case of extreme events that lead to the grid collapse and to massive blackouts in several cities. This is usually tackled by strengthening the energy infrastructure, however, the users' behavior and lifestyle will strongly influence the system capacity in stress conditions. This study focuses on the understanding of these phenomena and particularly on the relevance of the users' perception of indoor comfort, assuming the IPCC projections as the basis for a future scenario.

**Keywords:** climate change; building energy performance; IPCC; thermal comfort; building energy consumption; +1.5 degree; cooling increase

## **1. Introduction Concerning the Impacts of Climate Change**

Since 1970, a significant increase in the global mean temperature and atmospheric carbon concentration has been registered, however, the relation with climate change was assumed as a global challenge quite recently. Despite many evidences of the interdependency of phenomena, the conventional approach to cope with climate change impacts is often locally tailored rather than considered in the global dimension. Climate change is much less relevant to the human condition than warming in cities [1] and it can strongly influence both energy production/distribution and demand in the built environment [2] while increasing the risks that extreme events can heavily affect power infrastructures [3]. Adaptation and mitigation actions are therefore to be considered within a broader perspective that often goes beyond the local borders [4].

The Paris Agreement (December 2015) [5] represents indeed a step forward for the 1997 Kyoto Protocol and the quite limited commitment coming from some major countries. The massive improvement of renewable energy solutions (RES), required to achieve the defined ambitious goals, is clearly linked to the energy demand for heating and cooling with relation to indoor and outdoor comfort conditions which are clearly affected by climate change impacts [6]. In order to ensure acceptable comfort levels [7], a number of protocols, standards, and regulations have been developed with relation to the building sector. IPCC's 2018 reports about climate change (CC) and buildings can actually be considered one of the most authoritative documents to properly approach the topic. Additionally, some specific considerations are required to properly discuss the progress of European countries and the United States among the major nations involved in the climate change challenge in the West world.

## *1.1. The 2018 IPCC's Report*

The periodic assessment of climate science by the Intergovernmental Panel on Climate Change (IPCC) since the early 1990s is an essential contribution to the achievement of a global ontology on climate change [8]. 2018 IPCC's report—"Global warming of 1.5 ◦C"—is a key document within this challenge, offering a projection of the impact that a global warming of 1.5 ◦C and the related global Greenhouse Gas emission (GHG emission) routes would produce compared to pre-industrial levels. The report highlights several impacts that could be avoided by limiting global warming to 1.5 ◦C instead of 2 ◦C or more (such as sea level rise on a global scale by 2100 within a limit of 10 cm, the decrease of coral reefs of 70–90% instead of 99%, etc.). With the purpose of strengthening the global response to the threat of climate change, limiting global warming to 1.5 ◦C requires rapid changes in so many aspects of society contributing to sustainable development and to eradicate poverty [9]. Two major issues are related to the built environment: the relation between the building and the city scale as well as their performances with reference to the energy demand.

## 1.1.1. IPCC and the Role of Buildings and Cities

IPCC establishes different approaches to consider climate change as a collective action problem, and among them, it turned into a story of global mean temperature foreclosing the range of possible response options. By translating the multi-layered problem-complex of climate change (climates multiple) into a unitary global problem (climate singular), IPCC paved the way for a single policy agenda: emissions control monitored through a UN coordinated policy regime [10]. Through the adoption of the 2015 Paris Agreement, the climate regime is today replaced by a more decentralized and isolated climate governance order. By inviting states to propose nationally-appropriate mitigation and adaptation responses, the Paris Agreement has distributed responsibility for climate action across multiple actors, arenas, and sites [11]. However, few methods are actually available to analyze implications of climate change on building energy use [12]. The understanding of the combination of different factors within the design and operation stages is still crucial in contributing to improve the performance level.

## 1.1.2. Building Energy Performance (BEP)

The building sector contributes up to 40% of global energy consumption [13,14]. Despite the efforts spent in the last decades to reduce emissions, the level of GHG in the different scenarios projections demonstrates a dramatic rise in the coming future [15]. In this context, buildings represent a critical piece of a low-carbon future and their response to climate change solicitations has to be reliably predicted in order to take effective strategic design decisions in the mid-term. The role of building simulation tools has increased in its importance, giving the chance to speed up the design process, increase efficiency, and compare a broader range of design options. Building performance simulation (BPS) has been a suitable tool for helping to reach solutions for better energy efficiency [16]. Simulation provides a better understanding of the consequences of design decisions, increasing the effectiveness of the whole system [17] and it is now becoming increasingly relevant in post-construction phases of the building life-cycle (BLC), such as commissioning and operational management and

control. Software simulations allow to predict the system behavior within unobserved conditions, allowing analysts to simultaneously consider the impacts on the overall performance [18]. During the simulations, many different strategies are evaluated to obtain the higher performance for a set of objectives (e.g., zero energy balance) [19]. The energy performance of the buildings is really influenced by heat transmission, thermal mass, solar heat gain through windows; thus, any action in these fields can be of help in reducing building energy consumption [20].

## *1.2. European Approach*

According to the IPCC Fifth Assessment Report (AR5), 32% of global primary energy was spent by buildings, producing 19% of global emissions in 2010. Currently, these trends are still growing, reducing the chances to meet the targets agreed in the 2020 Climate and Energy Package [21], extended to 2030 [22] and 2050 within a long-term strategy [23,24]. The main efforts spent at the European Union (EU) level to reduce energy demand are addressed to decrease the needs for heating while ensuring adequate comfort conditions [25]. This led the EU to introduce the Energy Performance of Buildings Directive (EPBD Recast II [26] and EPBD Recast III [27]) and the Directive 2012/27/EU [28] on measures to help the EU to reach its 20% energy efficiency target by 2020 [29]. Each Member State can acknowledge the directives according to the most suitable and effective conditions within its own context in order to meet the targets (nearly Zero Energy Building (nZEB)) and to become more resilient to future climate conditions [30]. However, the effects of climate change and the evolving weather variables are influencing this process, impacting both the local network development [31] and energy use. Following IPCC projections, the energy demand for heating decreases due to the temperature variations and to the building performance improvement, while the demand for cooling is growing due to the increase of cities mean temperature.

## *1.3. USA Approach and New York Greening Actions*

The recent withdrawal of the United States from the Paris Agreement requires a reflection on the situation of one of the biggest contributors to global emissions, which is also a country severely affected by the often dramatic consequences of extreme events [32]. Commercial and residential buildings account for about 40% of the primary energy demand in the US and respectively 9.9% and 5.4% of global GHG emissions.

Despite the decision at the federal level, some states, such as New York and California, continue their actions against the climate change autonomously. In September 2018, Senate Bill 100 (SB100) required the Renewable Portfolio Standard (RPS) for electric utilities from 50% to 60% by 2030 and further targeted 100% clean energy in all sectors by 2045 [33]. New York City has developed several scientific reports and local regulations for improving urban resilience to cope with climate change [34]. In 2018, the New York government recognized the urgency for renovating the energy network by providing micro-grids, to help the systems surviving natural disasters or energy demand peaks that recently brought to the grid collapse. Renewable energy is certainly part of these important environmental goals and the New York State energy plan of 2015 aims to shift from 11% [35] to 40% of energy needs by 2030. The "Reforming the Energy Vision" initiative [36,37] integrates technical, management, and marketing approaches to explore the compatibility of small-scale renewable technology with existing residential buildings, commercial, or public areas, serving local needs while remaining connected to the wider network system.

## Projections of Annual Climate Change in New York

After the dramatic events of Hurricane Sandy, the New York City Panel on Climate Change (NPCC [38]) developed a system to collect and update information on the climate risk for the city with the purpose to both address the recovery after the event and to improve the resiliency.

The climate change impacts could be both short-term (storms and floods) and long-term (gradual changes in outdoor air temperature and sea level rise). As reported in NPPC [38], the long term impacts will be extremely likely events in the state of New York. Average annual temperatures are expected to increase up to 1.5 ◦C (3 ◦F) warmer by the 2020s, up to 3.3 ◦C (6 ◦F) warmer by the 2050s, and up to 5.5 ◦C (10 ◦F) warmer by the 2080s [38]. By 2100, the growing season could be about a month longer, with intense summers (extreme heat and heat waves) and milder winters. At the same time, rainfall will also increase noticeably, 1–8% by 2020, 3–12% by 2050, and 4–15% by 2080. These changes are associated with an increase in greenhouse gases and global warming [39]. The frequencies of heat and frost waves, intense rainfall, droughts, and coastal floods in the seven regions of the state will be subject to change in the coming decades, as predicted by the global climate model. These gradual changes will bring several consequences on built environment, and its energy consumption. Due to the huge difference between the outdoor air temperature and indoor temperature, the cooling systems will be responsible for the potential increase of energy end-use consumption. This is the state of the art so far following the IPCC report scenarios.

For investigating the impact of climate change on building energy consumption, the use of building simulation software, together with a set of forecasted weather data, is essential [40]. Within this general context, the methodology framework reported in this paper focused on the effects of a renovation project involving an iconic 14-story residential building in Red Hook, Brooklyn, New York considering the IPCC climate future projections of +1.5 ◦C overheating. The impact of climate change on indoor thermal comfort in the residential sector under different scenarios was also evaluated and reported in this paper.

#### **2. Scope of the Research**

The paper reports the outcomes of research aimed to evaluate the implications of IPCC climate change projections in the construction sector, applied to a case study in New York City.

The scope is to translate the outcomes of the research activities into concrete and practical indications that might be of help in replicating and driving the design process to tackle the climate change issue. Energy demand for heating and cooling is analyzed considering three weather scenarios (1958, 2017, 2100) according to IPCC projections and tested on a case study in Brooklyn, New York. In this way, simulations can take into account the predicted evolution of climate change [41]. The proposed study is primarily referred to the residential sector, which is the most relevant share in the building stock and one of the key targets of many regulations and governmental measures but can easily be adapted to other typologies. The paper aims also to provide a reflection on the effects of climate change to the thermal comfort, and the resistance of clothing (expressed in clo, m2K/W) is used as a variable. The clothing resistance characteristics are expressed as an average value, i.e., they refer to all the clothing worn and not just some (e.g., only the shirt, etc.).

#### **3. Research Methodology**

The adopted methodology includes the calculation of Building Energy Performance Simulation (BEPS) for the case study considering three climatic scenarios. Weather data for the selected years are provided by Energyplus for 1958 and 2017—construction time and current condition respectively while 2100 follows the IPCC temperature increase projection.

The use of weather data in simulation software like Energyplus, Ies.Ve, etc., can come from different sources (e.g., satellite data, weather stations, climate models, etc.) whose level of accuracy and reliability of the data source may change.

For the purposes of our research objectives, which is the evaluation of the variation over time of the simulation results, we considered the use of the data in the software database for the years 1958 and 2017. This choice may be questionable, but in this case, their accuracy supports the reliability of the adopted methodology. The approach combining simulations and scenarios is quite consolidated in the scientific literature, however, the focus on the effects of a temperature increase of +1.5 ◦C and the related overheating represent a novelty.

The research methodology is described following three sections:


Figure 1 provides a flowchart of the research methodology (that is specifically designed to facilitate the replication to other case studies).

**Figure 1.** Flowchart of the research methodology.

The first phase of the process is to reach a stable configuration of the virtual model in order to assume the weather data as the key variable and then perform preliminary thermo-physical analyses to evaluate the thermal/energy behavior. With weather data being the key variable, it is possible to compare the different behavior and the energy consumption of the building in the three different scenarios investigating the effects of temperature increase and related impact.

A complete set of thermo-physical analysis is provided for each scenario (S1-1958, S2-2017, S3-2100). The set includes also coherent values concerning loads, energy consumption, and indoor comfort, which are useful indicators to evaluate the behavior of the building with reference to the summer conditions. The methodological approach can be easily adapted to any context and case study.

The investigated outputs include: (a) yearly energy need for heating and cooling (kWh/m2), (b) total electricity/natural gas (MWh) consumption, (c) total energy consumption, (d) indoor comfort evaluated considering the predicted mean vote (PMV) and the predicted percentage dissatisfied (PPD in percentage, %) when the heating, ventilation, and air-conditioning (HVAC) system is operating, (e) indoor comfort evaluated considering the PMV and PPD when HVAC is not operating and consequently focusing on the clothing relevance.

The geometry and configuration of the building were rendered using Integrated Environment Virtual Environment software (IES.VE [42]), as well as the energy simulation to investigate the potential impacts of temperature increase due to climate change.

## *3.1. Weather Data for Each Scenario*

The research methodology requires three sets of weather data [43] corresponding to the three investigated scenarios (Sc.1 1958, Sc.2 2017, Sc.3 2100). The number 1958 refers to the closest period respect to the year when the building was erected (1939) and then entered in the decade of ordinary operation, 2017 refers to the present time conditions, 2100 refers to the IPCC projection horizon. Weather files referred to 1958 and 2017 were respectively downloaded from Energy Plus (Energy Plus, 2019) and World Meteorological Organization region and Country. Weather data format is a plain text file \*.epw (EnergyPlus Weather) and the National Weather Service Forecast Office (National Weather Service, 2019) was used as a typical meteorological year (TMY) [44]. The third set (future projection) is generated based on the 2017 one, assuming an overheating of 1.5 ◦C dry-bulb temperature, from 2018 to 2100, reported in the IPCC protocol [9], obtained using Element software. This is the main assumption of the entire article: analyze what happens if the IPCC forecasts on temperature are applied to the building performance simulation. Table 1 allows to compare the temperature increase over the years between the three sets. It can be observed that the 1958's winter temperatures were more rigid than the 2017's ones and this is a main effect of the increased temperatures as a consequence of climate change and the heat island effect because, within 60 years, the NYC's built environment has changed drastically.

Energy demand for heating and cooling was analyzed and compared for the three scenarios (climate models) [45,46]. The proposed methodology can be applied to different places simply by replacing the initial climate conditions (.epw) of the site under investigation.


**Table 1.** Average outdoor temperature.

## *3.2. Limitation of the Methodology*

Research relating to climate change is focused on the effects of energy use, pollution, and environmental issues. The main objective of this paper is not to report the review of these studies, but to highlight the effects that it will have as a result of climate change both on the energy consumption of buildings (specifically for this case study) and for indoor comfort and, consequently, on the clothing insulation. Given the complexity of the research carried out, some methodological limits have been assumed to be able to extend and compare the results with other (future) research. The methodological limits concern:



The unavailability of original consumption data did not allow the validation of the virtual model according to real conditions, however, a standardized calculation method has been adopted and the same approaches can be adopted in any other case study in a different context.

## *3.3. BEPS of the State of the Art*

Energy simulations were performed using the Integrated Environment Virtual Environment software (IES.VE [42]).The building was modeled to fix the main geometrical features and shape, the boundary conditions, the heating and cooling systems, as well as the services and installations. The building was approximated to a volume 100 meters long, 10 meters wide, and 40 meters tall, corresponding to an affordable housing building hosting 300 apartments in its 14 stories. The study estimated the energy consumption in each scenario with the purpose to evaluate the impact of climate change on the entire building energy demand. The heating/cooling set-points are defined in 20 ◦C/27 ◦C, target temperatures within the range that is acceptable to 80 percent of the building occupants according to ANSI/ASHRAE Standard 55, Thermal Environmental Conditions for Human Occupancy. A virtual simulation model was created, and the outcomes of simulations were then compared. Simulations considered the thermal inputs given by internal gains. The same building model (geometry, thermophysics, internal gains, etc.) was used for the simulation purpose, but each simulation was associated to its own weather data file (1958, 2017, 2100) of the set climatic zone: New York Central Park (\*.fwt or \*.epw file). The increase of temperature due to climate change influences the energy consumption of the building, leading to worsening of comfort conditions.

#### *3.4. Thermal Comfort and Clothes Insulation*

The reference model about thermal comfort is still the one developed by Fanger [47] that focuses on two indices: predicted mean vote (*PMV*) and percentage people dissatisfied (*PPD*), which respectively refer to occupants' mean thermal sensation vote and the percent of people voting. The *PMV-PPD* model is a widely used design tool incorporated in thermal comfort standards [48,49] that can be equally applied to different building typologies and climate conditions. Nevertheless, the accuracy of the *PMV-PPD* model in predicting thermal comfort has been questioned through field studies in real buildings [50] as well as in laboratory studies [51].

With this reference, the reported study focused on the relevance of occupants' clothing with relation to their thermal sensations inside the building. Statistical analyses were performed with the aim to better understand how building occupants can achieve thermal comfort by adjusting their clothing level of insulation [52]. This is quite relevant, assuming the poor insulation characteristics of the building envelope and the low-income condition of most of the residents which limit the opportunity to reach adequate comfort levels.

The proposed neutral clothing model can be used to determine whether clothing adjustment can sufficiently offset indoor temperatures in naturally ventilated building contexts [53]. The driving question was therefore: which clothing allows to perceive a good comfort condition comparable to a *PMV* = *0* (neutral) in the scenario without HVAC? To provide an answer for each scenario (1958, 2017, 2010) *PMV* was imposed = 0 and the calculation was repeated by setting different clothing insulation values *Icl* (clo).

The HVAC system was turned off in order to create a neutral condition, otherwise the system operates to achieve satisfactory comfort conditions.

Clothing insulation (*Icl*) is an input variable for thermal comfort calculations, and appropriate clothing adjustment can widen the comfort range and reduce building energy consumption [54]. For people in sedentary activities (a metabolic rate of approximately 1.2 met), the effect of changing clothing insulation on the optimum operative temperature is approximately 6 ◦C per clo, which is much relevant.

## *3.5. Thermo-Economic Retrofitting Solutions for Improving the Building Energy Performance*

The thermo-economic analysis combines thermo-physical analysis with economic aspects to identify factors involved in the generation of energy costs. Highest inefficiencies and costs are related to the processes in which natural gas is burned, hence in the boilers. The presented analysis can be extended to other energy systems. The trend for the following years shows a continuous increase in world population, CO2 emissions, and primary energy consumption. For that reason, in the building environment, the design of efficient systems has become a primary objective for energy policies in most countries [55]. The starting assumption of the study is that the investigated solutions are expected to let the building reaches a lower consumption, according to the major rating systems, meaning a consumption not exceeding 15–20 kWh/m<sup>2</sup> on an annual basis and the fulfilment of a number of sustainable indicators. Such conditions are achieved through a combination of different design choices involving several parameters, which are not all under investigation, in a balanced contribution deriving from the building envelope characteristics and services/installations.

For the case study under investigation, three main retrofit solutions were considered.

Retrofit A solution envisages the replacement of windows, respecting the U-values thresholds, and adding an insulation layer in the inner side of the wall to maintain the original exposed brick facing of the building.

Retrofit B solution replaces the original natural gas installation for heating and the single units for cooling with an electric heat pump and adding a controlled mechanic ventilation in each blind space.

Retrofit C solution adds to the measures included in A and B solutions, the renovation of the apartments following the guidelines of New York City HPD (Housing Preservation and Development) Department.

The set includes also coherent values concerning energy consumption for cooling and heating, which are useful indicators to evaluate the change of building energy performance due to the renovation process. A first observation of the outcomes, gives the chance to considerer how relevant the installations replacement may be in terms of energy performance. The main goal is not simply to compare the energy consumption of each scenario (A,B,C), that would of course reflect the differences due to the renovation intensity, but to address reflections towards the relation between them and the building energy performance as a relevant concern to be properly taken into account during the retrofitting phase for reducing energetic costs of the entire building and following a more sustainable approach.

## **4. Case Study Description**

The case study is part of a social housing development in Brooklyn (NYC) (Figure 2), known as "Red Hook Houses", owned by the New York City Housing Authority (NYCHA), the major social housing developer of North America, who since 1934, provides affordable and qualitatively sufficient housing for the low-income population of New York. Since 1939, when it was erected, the buildings were never heavily renovated and only after 2012 Hurricane Sandy, some extraordinary maintenance activities were done to install new utility systems due to the flooding event. The reference building (Figure 3) is 14 stories tall and was constructed using a reinforced concrete frame system with an exposed brick cladding. The building counts 300 apartments respecting the minimum air-illuminating ratio.

New York City has a humid continental climate with mean annual precipitation of 127 mm (50 inch) [56]. The 2017 mean annual temperature was 13.58 ◦C, with 24.88 ◦C in July and 1.70 ◦C in December, the hottest and coldest months, respectively. The simulation settings were the same in all the three scenarios: each building model had the same internal footprint, window size and glazing properties, the same HVAC system, internal gains, and infiltration rates, as Table 2 summarizes.

**Figure 2.** New York City—Red Hook Houses in Brooklyn.

**Figure 3.** Zoom on investigated building.



## **5. Results**

The results of the study are about three different aspects:


## *5.1. BEP Results for Scenarios of Climate Change (1958, 2017, and 2100)*

Indoor temperature is one of the parameters used to assess people's health and their comfort level. With the HVAC turned on, the average internal temperature is in a range of 20◦C–27 ◦C all year; when HVAC is tuned off, the temperature varies from minimum peaks of 3◦C–4 ◦C, up to maximums of about 30 ◦C.

## *a. Heating and cooling energy need loads*

Figure 4 shows the room heating/cooling loads of all scenarios. In the first scenario (1958), the need for heating during the winter months is greater than the other two (2017, 2100), since the average outdoor temperature is lower.

Instead, in summer, the demand for cooling in 1958 was less than the one in 2017 and the one expected in 2100. Compared to 2017, the projection for the summer period always increases while consumption is much higher for heating. Figure 4 shows the relation between energy loads for heating and cooling, by month, compared with outdoor temperature, for each scenario year: 1958, 2017, and 2010. As we can observe, cooling load increase in the 2100 scenario following outdoor temperature, if we compare 1958 and 2100 scenarios, we observe a swift of energy loads from winter (250 MWh heating load in 1958) to summer (320 MWh cooling load in 2100), which is depending on climate change.

## *b. Energy*

Table 3 shows the increase of cooling load despite the decrease of heating loads in all scenarios due to the climate change. Despite this switch, the total amount of consumption remains unchanged. Moreover, Table 3 shows the relation between electricity and fuel use: increasing the temperature, the demand for electricity is higher, while the request for natural gas decreases.



## *5.2. Thermal Comfort in the Di*ff*erent Scenarios*

The existing clothing models are typically used to estimate a clo-value of a group of building occupants, which then becomes input data for *PMV* calculations following ISO 7730 [48] and ASHRAE 55 [49]. *PMV*-predicted percentage dissatisfied model, thermal neutrality (*PMV* = 0) is deemed as an ideal or optimal status where the number of 'dissatisfied' building occupants is minimum.

## *a. Indoor Comfort WITHOUT HVAC system*

The effect of climate change without HVAC on indoor comfort perceived by users is also investigated. Table 4 shows the *PMV* values (predicted mean vote) of the users when the HVAC system is OFF, correlated to the average external temperature. The results evidence higher value of *PMV*, from −3 (cold) to 3 (hot), meaning that people are dissatisfied. The results prove that installations/services play a very relevant role in users comfort perception; more than 90% of people are dissatisfied during summer.


**Table 4.** PMV–without HVAC system related to the average outdoor temperature (◦C).

We can observe in Table 4 the annual average value of thermal comfort index *PMV* increase from +0.37 (as "neutral" or "slightly warm" sensation) of 1958 to 0.96 ("slightly warm" nearly "warm" sensation) of 2100, this is a huge difference.

In detail, during the winter season, from October to March, index *PMV* change from −1.13 ("cool" sensation) in 1958, to –0.43 (slightly cool) in 2010, so we suppose a less energy need to heat in the 2100 scenario. During the summer season, from April to September, the index *PMV* change from +1.88 ("warm" nearly hot sensation) to +2.35 corresponds with a very hot sensation, nearly a heat stress value.

These simulations are without HVAC systems, so the sensation depends only by building, so it is evidenced by the main role of heating and cooling equipment and their energy consumption to guarantee a neutral thermal comfort sensation (neutral if *PMV* equal to zero). Following the above approach, not only does HVAC have a role, but also clothes in indoor, described in the next paragraph, we would like to discover which dress people put on their body to feel a neutral thermal comfort, when the *PMV* index is equal to zero.

## *b. Relevance of clothing*

For ensuring good comfort condition comparable to a *PMV* = 0 (neutral) and metabolic activity is equal to 1 met (seated activity) in no operating HVAC scenario, a *PMV* = 0 was imposed and the calculation was iterated with different *Icl* (clo) (clothing resistance) for each case. Table 5 provides a summary of the outcomes where it is clearly evident that during the summer period, comfort is acceptable only wearing shorts and a t-shirt (the perception is the one of a clearly overheated environment), while during winter, a polar equipment would be needed to achieve minimum livable conditions.

The adopted approach intentionally stresses the focus on the role of clothing and on the perception of users to remark the dependency of comfort conditions from HVAC and generally from services and installations. This is strictly connected to the inefficient and low performing condition of the building envelope which—at the stating conditions—is highly inadequate to meet the energy demand reduction that is expected in the very near future.

In Table 5, it is possible to observe the annual average value of clothes (or dress thermal resistance) to guarantee a neutral thermal comfort (*PMV* = 0) that corresponds to 2.01 clo (Underwear with short sleeves and legs, shirt, trousers, jacket, heavy quilted outer jacket and overalls, socks, shoes, cap, and gloves, following ISO 9920 Annex A [57]) in 1958 and 1.58 clo (Underwear with short sleeves and legs, shirt, trousers, vest, jacket, coat, socks, and shoes, following ISO 9920 Annex A) in 2100, with a decrease of 0.40 clo of clothes thermal resistance, which corresponds to a sweater or jacket. In detail, during the winter season, from October to March, clothes thermal resistance (*Icl*) change from 3.22 to

2.67 clo with a gap of 0.55 clo, while in the summer season, from April to September, the gap is 0.30 clo, which corresponds to a t-shirt.

**Table 5.** Clothes insulation (measured in clo) without HVAC and *PMV* = 0, related to average indoor temperature.


On the other hand, in Table 6, there is a comparison between the *PMV* index that the users would have if the indoor temperature would have been 20◦C with the HVAC system OFF. The results of these simulations are the values of clothes thermal resistance (*Icl*), which shows how a user might be dressed for having this *PMV* index in this environment.

**Table 6.** Clothes insulation in a *T* = 20 ◦C ambient with a *PMV* index of the HVAC system OFF simulations.


## *5.3. Thermo-Economics Evaluations Concerning Retrofit Process*

Simulation is credited with increasing efficiency and enabling the comparison of a broader range of design options, leading to a more balanced and optimized design. Simulations provide a better understanding of the consequences of design decisions, which are supposed to increase the overall building energy performance. The starting assumption of the study is that the building has to be renovated to meet a 15–20 kWh/m2 yearly energy demand threshold. Therefore, the renovation work costs, and the investment needed to support the process were analyzed and compared among the different solutions.

Table 7 shows the results of the simulations for each scenario. The current energy performance of the building is 155 kWh/m<sup>2</sup> per year, which is a quite high level of energy demand considering the target.


**Table 7.** Results of different scenarios.

Solution A—includes the replacement of windows and the introduction of an additional insulation layer in the inner side of the wall—lead the energy performance to 94kWh/m2 per year, requiring a quite relevant investment for renovation.

Solution B—improves solution A and operates replacing the existing heating/cooling system with an electric heat pump and adding a controlled mechanic ventilation—reduces the energy demand to 15 kWh/m2 with about 90% of energy saving compared to the current status.

Solution C—improves solution B and simply adds the apartments refurbishment according to the HPD guidelines, achieving an overall energy performance of 13 kWh/m2 per year.

## **6. Discussion**

## *6.1. BEP of Current Conditions in Di*ff*erent Scenarios (1958, 2017 and 2100)*

Looking at the outcomes of the performed analyses of the three scenarios (1958, 2017, 2100) is evident how loads, consumptions, and comfort are influenced by climate data. The research allowed to understand the variations correlated to outdoor/indoor temperature dealing with the building behavior (loads), the energy data (consumption), and the comfort data (PMV/PPD).

Comparing the situation between 1958 and 2100, reported in Figure 4, it can be noticed that there is an increase in the building's cooling needs of 50% in the 2100 prediction with respect to 2958 (+14% in 2017 with respect to 1958), which clearly addresses the design challenge toward the summer period to find adequate technical answers for reducing the overheating effect. In 1958, energy demand for heating was 19%, while in 2100, it is expected to decrease to 12% (see Figure 4) with a consequent increase in the share for cooling, with a radical shift in the energy use and building thermal behavior compared with a conventional situation. Renovation works will reduce the energy demand for heating (fuel), replacing the existing boilers), but the request for electrical power will increase exponentially, impacting on the energy infrastructure. The increase of outdoor temperature will impact on indoor conditions and consequently on comfort condition for inhabitants and their related perception, which will be dependent on HVAC and installations.

## *6.2. Thermal Comfort*

To understand how thermal comfort varies if HVAC is not operating, the users' perception was investigated with the purpose to better address the solutions dealing with the building envelope main features and performance. The increase in the share of energy demand for cooling is of particular interest at the European level where the use of the HVAC during the summer period is a quite recent need. Historic buildings are usually massive structure with natural ventilation and the wide building production between the 1960s and the 1980s was not originally air-conditioned. This represents a relevant cultural difference with the United States where the use of the HVAC is consolidated over a century. Figure 5 report a infographics about thermal comfort in each scenarios.

The choice to work on the NY case study reflects the will to work on a consolidated situation for both winter and summer energy demand regimes, assuming that the huge increase in the demand for cooling will represent a revolving trend in EU for the future.

The scientific literature provides a large production of studies about building thermal behavior and energy performance as well as about the retrofitting and renovation solution, however, the impacts of the projections in temperature trends reported by IPCC still represent an open field of research. The novelty of the proposed study lies in considering the interrelated factors focusing on the effects on thermal comfort perception by the users and on the potential deriving changes in life conditions and styles.

**Figure 5.** Infographic about thermal comfort in each scenario considering the HVAC system ON and OFF.

## *6.3. Thermo-Economics Evaluations after Retrofitting for Improving Building Energy Performance*

Among the objectives of this study, there is the understanding of the impact of building renovation on heating/cooling demand with relation to the temperature increase projection. Three building renovation solutions were developed with relation to the 2017 scenario. Assuming the current status as the starting point, solution A replaces the existing windows with new ones, respecting the directives U-value (W/m2K) thresholds and adds a new insulation layer, achieving a savings of 40%.

Solution B replaces the existing boiler for heating with an electrical heat pump for both heating and cooling, combined with a controlled mechanic ventilation, achieving a savings of 90%. Solution C adds to B the refurbishment of the apartments according HPD's guidelines.

The total energy performance shifts from 155 kWh/m2 (of the current status) to 13 kWh/m2 per year. The results are obtained modeling only the basic parameters involved such as insulation, window glazing and installations, but other key elements like the level of shading, building orientation, and window size, which were kept constant, can be varied to further improve the outcomes. The study assumed the limitation of preserving the exposed brick façade of the building, but in different context, a more performing insulation on the outer skin can be considered for improving the performances of the building envelope.

## **7. Conclusions**

This research assumes that energy simulation and analysis must be carefully considered and included during the renovation process aimed at improving building energy performances, to achieve effective savings and more efficient design solutions in terms of comfort. The proposed methodology has, as the main objective, the replicability and can be helpful both in the case of renovation and in case of new construction, aiming to consider IPCC projections and the related effects on people's health and wellbeing. The method adopted also allows incremental numerical data to be obtained. This study evidences the risk of grid collapse and blackouts in case of demand peaks for cooling as

it happened in NYC in July 1977 and more recently in July 2019, paralyzing the city for more than 3 h. The main answer is, of course, to strengthen the energy infrastructure as the NYC authorities are already doing, however, a modal shift in approaching renovation and comfort evaluation could be of help in supporting a cultural change, which will be an unavoidable consequence of IPCC projections.

The outcomes of the case study confirm that climate change will significantly affect the future improvement of building energy performance and indoor thermal comfort (especially ventilation), becoming a global challenge that will require to work on thermal insulation and reflectance, but also on ventilation parameters that have a very relevant role on the demand for cooling.

That climate change and improvement of thermal and ventilation parameters significantly affect the demand for cooling—in time, it will be a huge global problem.

Further developments of the proposed study are certainly needed to explore the effects on different building typologies as well as on the inhabitant's reaction considering different climate contexts and cultural background.

**Author Contributions:** Conceptualization, K.F. and J.G.; methodology, K.F and J.G.; software, L.F.; validation, K.F., L.F. and J.G.; formal analysis, L.F.; investigation, L.F.; resources, J.G.; data curation, K.F.; writing—original draft preparation, L.F.; writing—review and editing, K.F. and J.G.; visualization, L.F.; supervision, K.F.; project administration, J.G.; funding acquisition, J.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding

**Acknowledgments:** In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).

**Conflicts of Interest:** The authors declare no conflict of interest.

## **Nomenclature**


## **References**

1. Tol, R.S.J. Population and trends in the global mean temperature. *Atmosfera* **2017**, *30*, 121–135. [CrossRef]


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **A GIS-Based Methodology for Speedy Energy E**ffi**ciency Mapping: A Case Study in Bologna**

## **Jacopo Gaspari \*, Michaela De Giglio, Ernesto Antonini and Vincenzo Vodola**

Department of Architecture, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy; michaela.degiglio@unibo.it (M.D.G.); ernesto.antonini@unibo.it (E.A.); vincenzo.vodola2@unibo.it (V.V.)

**\*** Correspondence: jacopo.gaspari@unibo.it

Received: 12 March 2020; Accepted: 22 April 2020; Published: 3 May 2020

**Abstract:** The paper reports a methodology developed to map energy consumption of the building stock at the urban scale on a GIS environment. Energy consumption has been investigated, focusing on the shift from the individual building scale to the district one with the purpose of identifying larger homogenous energy use areas for addressing policies and plans to improve the quality and the performance levels at the city scale. The urban planning zoning concept was extended to the energy issue to include the energy behavior of each zone that depends on the performance of its individual buildings. The methodology generates GIS maps providing a district scale visualization of energy consumption according to shared criteria. A case study in Bologna city (Italy) is provided. In the specific case, the last update of Emilia-Romagna regional urban planning regulation required a mapping action regarding energy efficiency of homogeneous urban portions defined by the General Urban Plan. The main achieved results are (a) a methodology to identify homogeneous areas for analyzing energy consumption; (b) an updated energy map of Bologna Municipality.

**Keywords:** energy efficiency mapping; energy zoning; energy performance certificate; geographic information system; GIS-based methodology

## **1. Introduction**

During the last ten years, a number of regulations were introduced at national and local levels across Europe to acknowledge the EU directives concerning energy efficiency [1,2] and to boost energy savings measures in each member state. In addition to the Italian national regulation, Emilia-Romagna Region has recently updated the Regional Urban Planning Law (LR 24/2017) that states each city of the territory is required to perform a mapping action to detect the low quality stock of existing buildings, particularly the ones not fulfilling the minimum thresholds of energy efficiency and seismic safety (art. 22, par. 6). The request is not associated with any guidelines for performing the mapping action, leaving each municipal administration (MA) free to adopt its own methodology and to display the outcomes accordingly. This makes the possible results often very heterogeneous and hard to compare, depending not only on the adopted approach but also on the quality and reliability of the data to be processed.

This paper summarizes the outcomes of a study that was commissioned by the City of Bologna to define a comprehensive and possibly replicable approach to answer the request while achieving a useful methodology to address the planning actions of the near future, taking into account the energy issue as a key priority in the city development. The proposed approach aims to provide a solution for a quick energy mapping with the purpose of reducing the workload usually needed to face a very time-consuming activity that deals both with dedicated data collection and processing. The method therefore considers the use of already available datasets. As evidenced in the literature [3–8], data processing could take an extremely long time, due to the different data collection criteria and above all

to the frequent lack of long-term plans for data collection campaigns within the public administrations. This has led to reflect on how mapping is usually conducted, according to the purpose. In most cases, mapping actions are generally based on a precise collection of data related to very appropriate basic units in order to increase the reliability level. However, in this specific case, this would drive the approach to read phenomena at the building scale, rather than at the city scale (which is the typical level of planning actions). Consequently, this study approaches the mapping action accepting some limitations and approximations with the objectives of accelerating the process and providing a comprehensive picture (which can be appropriately updated) in a timely manner of the energy demand at a broader scale.

In order to meet the Bologna Municipality requests, this research looks at energy efficiency from a different perspective, shifting the scale of investigation from the building to the district scale. Energy efficiency is traditionally surveyed at the building scale by analyzing the building envelope, the technical installations and the whole-building energy performance. However, this does not reflect the scope of the Regional Law request, which is basically oriented to understand how energy efficiency is distributed within the urban fabric and to reveal and the energy consumption trend. Thus, the study associates energy mapping with the zoning concept that is traditionally used in the urban planning disciplines [9,10]. This requires relating energy demand level to each city block, identified as the minimum unit of the mapping action. The scope is to visualize an energy zoning that, reflecting the current trend of energy demand of the city blocks, will allow MAs to improve their basic knowledge level and to increase the effectiveness of the possible actions to be taken in the near future through the urban regulations. According to this scope and general framework, the research initially investigated input data availability. This was particularly challenging due to the different data sources and related quality as well as to the different update levels of the datasets produced over time.

The outcomes of the study are (a) the definition of a methodological approach for energy mapping based on the energy zoning concept and (b) the visualization of energy zoning of the Bologna city case study via GIS environment.

The study represents a pilot experience that, despite facing several limitations and barriers, tries to offer an original approach to the topic, focusing on a broader vision to support future planning actions and defining a methodological backbone that can be refined and integrated with new data to improve its quality and reliability.

#### **2. Background Knowledge and State of the Art**

Despite the scientific literature offering a consolidated knowledge on surveying of energy efficiency at the building scale, experiences at district or city scales are still limited and are mainly associated—at least across EU—to wide research projects funded under the umbrella of EU framework programs. Among them, "STEP-UP Strategies Towards Energy Performance and Urban Planning" is one of the first outlining the need to focus on large-scale visions connecting initiatives to support renewable energy source (RES) use with demand trends and player engagement [11]. Dedicated studies operating at the city scale are provided by Caputo et al. [12], Theodoridou et al. [13] and Heiple et al. [14], among others [15].

However, any vision at the urban scale requires data regarding energy consumption to be collected, processed and then displayed with relation to a city map that is typically a georeferenced queryable graphic representation in a geographic information system (GIS) environment (as happens in many other sectors, such as mobility and green infrastructure). According to the most shared definitions, georeferencing is the process of assigning real-world coordinates (i.e., related to an official geographic or cartographic reference system) to a specific object that can be a pixel of raster data [16] or a vector entity such as a point, a line or a polygon [17–19]. All possible input data have to include coordinates or linkable information to available georeferenced geometries, such as cadastral data or street name and building number [20]. The objects can differ in size or typology, affecting the level of detail of the investigation (buildings, cadastral parcels, blocks, etc.). The level of detail consequently has to be

set according to the mapping purpose and based on the available input data, which must be reliable and accessible. An overall data analysis is needed to set the process and ensure that the outcomes can be clearly understood by the users [21], while adhering to the current regulations regarding privacy in terms of data protection (as data can potentially give information on users' behaviors or living preferences) [22].

GIS is often used in the energy domain to visualize consumption according to specific parcels or filtering data by building typologies [23,24], to address retrofitting actions [21,25], to predict the potential economic and environmental impacts of the adoption of RES use in a specific context [20,26], to plan specific energy infrastructure initiatives [27] or to obtain a projection of atmospheric emissions due to energy consumption [28]. Some studies use GIS technologies to provide energy mapping of built units (individual buildings, blocks, urban fabric portions, etc.) [25,29], offering good evidence of its effectiveness for the purpose and ensuring a precise data georeferencing, a prompt update over time and an easy way to visualize complex information. Since the entailed energy flows are the basic input data needed to draw a GIS map of an area's energy efficiency [23,30,31], the availability and the level of detail of data regarding the energy performance of each considered entity affects both the precision of the model and its reliability [32–34]. According to Lin [25], two main sources can be theoretically used for the scope: on the one side, real energy consumption data can be gained by the energy supplier metering system, assuming that real-time metering devices are installed and that there is a cooperation agreement with the supplier; on the other one, energy consumption can be extracted from energy bills, assuming that users are able to provide this information [32,35]. Unfortunately, there are at least two main critical barriers that obstruct the concrete application of this theoretically optimal approach.

(1) Specific or individual information and data about energy consumption are confidential, and any potential use is regulated by personal data protection [36]. Consequently, they are not easily accessible without the massive and complex involvement of all users potentially involved in the area under investigation [22].

(2) Aggregated data can be more often accessible (under request to the responsible entity); however, the way in which data are aggregated into sets may differ from the physical aggregation of the involved building unit or may be recorded at a scale which does not fit the purpose of the study [37,38].

According to Tronchin and Fabbri [39], energy consumption can be alternatively obtained from energy performance certificates (EPCs), which must be associated to any new or renovated building unit, reflecting the predicted average energy demand based on the technical and constructive characteristics of the building unit according to an official calculation code [40]. EPC data may not exactly match real consumption patterns, and eventual deviations have to be verified by sample tests. Despite being an indirect way to estimate energy consumption, this is a good and more easily accessible option for gaining input data, even if it might not cover the whole stock included within a territorial administrative area. Accurate and systematic information regarding the energy behavior of the building stock including large shares of old and very old buildings is often unavailable in most European urban areas.

Since comprehensive knowledge about energy demand at the urban scale is a strategic element for implementing urban policies in many sectors, several indirect estimation methods have been developed to overcome the lack of direct consumption data. Among them, TABULA—which is the result of an EU-funded project under the IEE program 2009–2012 [41]—established a harmonized catalog of building typologies for the European residential building stock, assigning a typical energy consumption level to each identified category. The typological classification criteria are based on the age of the building, the number of floors and some constructive features that can be identified quite easily by a speedy survey, allowing the application of the methodology to a wide number of cases and keeping the reliability of results within acceptable levels of approximation [42,43]. A good coverage of the required data concerning the involved building stock is an essential factor in limiting the unavoidable imprecision rate of this indirect methodology. Since the results typically refer to the energy demand of a single building, homogeneous coverage is even more relevant when the main

objective is to analyze the energy consumption at the scale of aggregated buildings, where potential gaps may seriously affect the reliability of outcomes [44].

## **3. Data Collection and Methodology**

Even though the present research was generated by a specific request at the regional level, the study was developed within a general theoretical approach with the purpose to offer a "speedy method" to allow MAs to consider the energy performance of the urban stock at a broader scale that can possibly be replicated in different contexts. As evidenced by the scientific literature consulted, a shift from the building to the urban scale in energy consumption analysis is required to better address the future actions in city development and transition to a low-carbon environment.

The study is based on the idea of translating the energy zoning concept into energy maps—to be typically displayed at the district scale—with the purpose of obtaining a tool for easily visualizing energy consumption trends and distribution within the territory under investigation. Accordingly, the GIS environment is considered a powerful way to correlate data and information under investigation with georeferenced data. The GIS environment allows the data to be easily updated and enables the them analysis outcomes on be displayed on the city map.

The methodology was based on two main types of input data strictly related to the building entity that—being the object to which energy performance/demand is associated with—was assumed as the basic unit of the process:

(a) The first required input data typology deals with vector files of the geometrical units to which energy data correspond to. This is a compulsory element needed to locate the basic building unit in the map and univocally associate the related energy data within a GIS environment.

(b) The second required input data typology regards energy consumption of the basic considered units. It has to be noted that this unit may differ from the building as a whole, according to the typology and function.

## *3.1. Data Collection*

Vector files have to possibly cover the whole city administrative territory (or at least the one within the investigation boundary). The vector files shall refer to the geometric definition of the buildings, the cadastral parcels, the city blocks, etc., including a set of related topographic information. Generally, the basic unit is associated to individual buildings. These vector files are typically owned by the City Technical Offices for many different purposes and created in different phases according to the need and the scope. It is relevant to note that they frequently present some discrepancies depending on the way in which geometrical data were collected and the files generated. However, considering the whole scale of the process, this can be considered a minor issue.

Energy consumption data represent the key variable of the process and, methodologically speaking, real data are certainly to be preferred, where available, in order to increase the reliability of outcomes. As Figure 1 shows, the availability or unavailability of real data determines the steps of the methodology. Unfortunately, real data are not so frequently available and accessible, as the ownership in most cases is in the hands of the energy supplier, who has to comply with data protection regulations.

Even though specific agreements can solve these limitations (as has happened in some cases), past experiences and the literature [45–48] suggest considering real data to be unavailable. The present study aims to face the most recurring situation where many barriers obstruct the use of these data; thus, keeping in mind the speedy approach of the method, a worst-case scenario is considered. An alternative source of input data is therefore required. Energy performance certificates (EPCs) represent a viable solution. When approaching this data source, the distribution within the city administrative territory has to be carefully checked to make the process feasible. Once input data are secured, the linkage through coordinates or georeferenced information in the GIS environment must be explored, and the coverage within the territory under investigation must be quantified.

Assuming the use of EPCs as primary data source related to energy demand, the linkage to the vector polygon must be consistent with the administrative entity, usually the cadastral parcel, with EPCs being administratively associated to the building property (which typically refers to the cadastral system). It has to be noted that frequently the cadastral parcel does not perfectly match the building geometrical definition, and the vector files may consequently differ. In the case of more units being included in one building or cadastral parcel, EPCs can be mediated considering the coverage and the boundaries of the selected unit. The decision regarding the file to be used depends on quality of the available files made available by the MA.

**Figure 1.** Methodology workflow.

#### *3.2. Methodology*

The methodology requires obtaining a unique shapefile embedding all original or mediated energy efficiency values univocally associated to the selected basic unit (buildings, cadastral parcels, blocks, etc.).

As the main objective is not to provide a picture of a constellation of individual building energy performance but rather to focus on detecting homogeneous or heterogeneous areas in energy demand within the city territory, a shift from the building or the cadastral parcel geometry to the one of the city block is required. An intermediate required step is to properly define the city block. The definition comes from the urban planning discipline trying to overcome some semantic differences attributed to the urban basic units country by country. Thus, the city block is intended as an ensemble of buildings and in-between spaces (courtyards, private passages, gardens, etc.) delimited in the perimeter by the typical public viability and representing the typical urban unit of larger neighborhoods or districts.

The shift from the basic unit data association to the city block is operated calculating the so-called K index, which represents an average value obtained from the summation of the energy performance of all the available EPCs included in the city block (mediated according to the related usable area) over the city block surface. The K index is initially calculated as the weighted average of the total primary energy (Eptot) needs of the building units belonging to the same cadastral parcel, with a weight equal to the useful surface. In the case of generalizing K-values from building level to block level, significant differences may occur in the building size and typology within the same block, and the area/size of a building can be used as a weight for calculating the average K-value.

Subsequently, the K-value at the block level is obtained from the simple average of the K index, previously calculated at the building level (or the cadastral parcel level). Therefore, K is intended to be a

geometrical correlation factor between the cadastral parcel unit (or the selected basic unit) and the EPC database (which is also based on a geometrical definition). This allows easily updating the aggregated index if additional data become available within each city block reflecting the improvements made at the individual building scale. At the same time, this allows associating an average behavior with a larger portion of city, providing the MA with a broader picture of the situation sector by sector.

The outcome is a vector map showing the energy efficiency associated to each polygon. The vector data include an attribute table containing all the information related to the considered geometries, which allows extracting specific information related to particular conditions. The map may include some unclassified polygons, due to the eventual lack of input data regarding the EPCs (cadastral data, geometric data), that appear as neutral but may be updated when the related input data become available. The energy use intensity can be associated to other maps (typologies, functions, density, etc.) in order to better define specific improvement and development policies, depending on the MA programs.

The novelty of the proposed methodology does not deal with data processing—as the intention was to adopt currently available tools to facilitate the replication and adoption of the process—but lies in the approach to mapping (from the building level to the block level) considering the urgency of a broader vision and the need of a less time-consuming process to support decision-makers and planners in driving measures at the city level. The contributions offered by the proposed methods to those operating in this sector deal with being a feasible process based on available resources in a quite speedy way, a viable solution to overcome frequent existing barriers (e.g., limited access to real data, limited data coverage, limited precision of vector files) and a replicable process in different contexts using quite comparable input data. The proposed methodology reflects a very flexible approach, designed to be easily replicated, since both the input data processing and the scale of representation can be modified, consequently adapting the process to the local framework in order to achieve comparable maps displaying the available data content [49].

Even though the methodology may suffer from some approximations and loss of data (mainly due to lack of a complete coverage), it is designed to easily accept adaptations or updates. The increase of data availability and accuracy can improve the quality of outcomes and mapping process definition, giving the MAs a tool to quickly monitor the mid- to long-term progress and impacts of the adopted policies.

In order to validate the process and the obtained map, different input data must be assumed [20] that possibly adhere to real energy consumption. Considering the abovementioned limitations and the complexity of a large-scale process, the validation step can be performed on sample test sites where information and input data can be more easily accessible and available. A case study application has been performed within the city of Bologna.

## **4. Bologna Case Study Application**

According to the request of the MA, the defined methodology has been applied to the case study of Bologna, a medium-large-size city located in lower northern part of Italy (Figure 2). With approximately 400,000 inhabitants (1 million considering the whole metropolitan area), Bologna is the seventh most populated Italian city [50]. As the capital city of Emilia-Romagna Region, it has an extent of approximately 140 km<sup>2</sup> and a density of 2780 inhabitant/km<sup>2</sup> [50] Bologna is renowned for having one of Italy's largest and most well preserved historical city centers.

**Figure 2.** Bologna Municipality (Italy) location.

The urban plan adopted in the late nineteenth century drove the expansion of the city outside the medieval wall ring influencing the city development since the end of the Second World War, when large portions of urban fabric mixing residential and industrial settlements were newly built on the north, east and west sides, often creating dense districts [51–53]. A new general urban plan was adopted in the early 1960s to address further expansions and to mitigate the lack of green and district infrastructure. During the last twenty years, the city of Bologna was characterized by a less intensive building activity more oriented to meet the goals and the key elements of sustainable construction and therefore acknowledging the national regulations that were introduced to adhere to the EU directives. Furthermore, in 2015 Bologna adopted the so-called Bologna Adaptation Plan as the result of the BLUE AP project (Bologna Local Urban Environment Adaptation Plan for a Resilient City), funded by the EU under the umbrella of LIFE+ program (LIFE11 ENV/IT/119), to address future development actions towards more resilient and less energy-consuming solutions to cope with climate change [54].

The update of the Regional Law on Urban Planning (LR 24/2017) therefore represents the last step of a process deeply focused on properly facing the challenge of greatly decreasing energy demand in the coming decades. The study reported in this paper represents the outcome of an intense dialogue with the Bologna administration during the last year and a half.

## *4.1. Preliminary Phases*

During the preliminary phase, the research team and the MA discussed the possible data sources according to availability and accessibility. Real energy consumption data were assumed as unavailable, as this data typology is not owned by the MA and there is no specific agreement with the energy supplier to access it. The initial request of the MA was to estimate the energy performance level considering the year of building construction and both the surface (S) and the volume (V) of each building. The first attempt of association using the TABULA database [55] gave unsatisfactory results, demonstrating that the proposed methodology requires input data regarding energy consumption. Once this was clarified and the MA commitment in this direction was obtained, some key priorities were defined: (a) the level of knowledge about energy performance had to be explicitly related to the basic unit in terms of input data to be made available; (b) a stable and possibly univocal definition of the polygon, corresponding to the building unit, had to be achieved.

Thus, EPCs were assumed as input data regarding energy consumption, being aware that they could not completely cover the whole building stock but having in mind that they are continuously updated and implemented, allowing the methodology to be constantly refined and increased in coverage when new input data become available.

The MA agreed to provide the most updated vector files, regarding both the buildings and the blocks, as well as the EPCs concerning the buildings located within the city's administrative boundaries.

The EPCs files were collected in separate sets, organized according their issuing period, namely 2009–2015 (EPCs from 2009 to 1 October 2015—81,350 records), 2015 (EPCs from 1 October 2015 to 31 December 2015—3265 records), 2016 (full year—11,649 records), 2017 (full year—9973 records) and 2018 (full year—5624 records). The distinction within 2015 is due to the introduction of a new regulation in force from 1 October 2015 (Ministerial Decree 26/06/2015) concerning [56] (a) new elements (such us aeration, air-conditioning, artificial lighting) to be included within the Global Energy Performance index calculation (later called Eptot) and (b) the use of kWh/m2 \* year as energy performance unit of measurement for all building typologies (while before 1 October 2015, the index was referred to as the volume unit (KWh/m3 \* year) for nonresidential buildings (called E1, including several subclasses)). Due to data protection regulations, EPCs were provided without including the building address and street number, only including cadastral data for GIS linkage purpose.

An EPC database, with cadaster and full location files, was provided to the Municipality by the Emilia-Romagna Region (who is the subject in charge of collecting these data) for validation purpose in sample areas only and with a strict nondisclosure agreement. Thus, the entire first stage of the study was carried out referring to cadastral data only.

## *4.2. First Stage*

The first stage of the process aimed at computing the energy efficiency K indexes to be associated with the map geometries. This index refers to the Eptot values and the total usable area belonging to each individual cadastral parcel. The index is thought to mediate the potential lack of some EPCs in the same cadastral parcel (the lack could be amended in the future when the missing data become available). The calculation process allows monitoring the loss of data in each cadastral parcel. The K indexes were calculated based on EPC data and using Microsoft Excel and QuantumGIS, according to the following steps:

(1) Each EPC dataset was filtered to delete records (almost 3%) including errors (i.e., zero or missing value for heated volume field, usable area field, cadastral parcel field, or formally incorrect values, etc.)

(2) The EPC datasets were merged into a single file (111,861 records), detecting and erasing EPC duplicates (almost 1%) associated to the same building unit to keep the most updated one.

(3) Eptot values determined before 1 October 2015 for E1 building class were converted to express the unit measure in usable floor area instead of heated volume (this is to amend the change in the regulation that occurred).

(4) Eptot values (almost 1.5%) over the thresholds of 500 KWh/m2 \* year for residential buildings and 800 KWh/m<sup>2</sup> \* year for nonresidential buildings were excluded, assumed to be out of scale based on regional guidelines and the literature [57]. At this stage, the total number of EPCs suitable for K index calculation shrunk to 106,469.

(5) As reported in the literature [58–63], most of energy efficiency indexes take into account the building envelope characteristics, the heating/cooling and services systems and the concurrent features in order to determine the energy demand. EPCs comply with calculation standards, and the proposed K index aims to mediate the sum of energy performance of the basic units within each cadastral parcel. The energy efficiency K index was calculated as follows (Equation (1)):

$$\mathbf{K} = \frac{\sum \left( \mathbf{E} \mathbf{p}\_{\text{tot}} \times \mathbf{U} \text{sable area} \right)}{\sum \text{usable area}} \tag{1}$$

where K (KWh/(m2·year)) is the energy efficiency index, Eptot (KWh/(m2·year)) is the energy efficiency value for single unit with EPC, - usable area is the total usable area (m2) or the sum of all the useful

surfaces with available EPCs within the same cadastral parcel and Usable area (m2) is the single unit floor surface.

(6) K index values were checked to delete eventual errors (less than 1%).

The obtained K indexes computed for each cadastral parcel were then ready to be transferred to the GIS environment. For this purpose, the merged EPCs file (steps 2–4) with 106,469 records, the cadastral parcel vector data (73,984 polygons) and the building vector data (41,010 polygons) were used.

The obtained K index can be consequently associated directly to the cadastral parcel or to all the buildings included in it.

In the first case, a one-to-many relationship was established between the cadastral parcel attribute table (A—father) and the EPC table (B—sons). As the primary key is a unique identifier, the foreign key can reliably reference it as one record [64]. The A primary key was obtained by joining the cadastral sheet and the cadastral parcel fields.

In the second case, a new one-to-many relationship was added. It was established between upgraded cadastral parcels vector (A) and building vector (B). All entities to which an EPC is not associated (such as churches, historic monuments, kiosks, etc.) were previously excluded.

Finally, to add the K index field to building vector, the join function was applied between the building attribute table and the cadastral parcels attribute table.

## *4.3. Second Stage*

In order to adapt the model for urban planning purposes, the energy consumption trends were projected at the urban fabric level in a second stage. The coefficient K was mediated at the block scale. The block is defined as a portion of urban fabric surrounded by public streets whose boundaries are classified from an administrative point of view [65]. However, other scale options might be considered in different contexts according to specific characteristics and goals. The decision was discussed and agreed upon with Bologna MA. This does not limit the replication elsewhere nor the validity, as both are mainly dependent on the selected data source availability. The file containing the geometrical definition of the blocks was provided by Bologna MA. The process was managed by QuantumGIS software according to the following steps:

(a) The block identification code was associated to each building by using the position tool.

(b) For each block, the mean K-value (starting from K-values of buildings included of the same block) was calculated (KBLOCK index).

(c) The new one-to-many relationship was accordingly applied. It was established between block vector (A) and building vector (B), so that a single record of the block attribute table (A) matched many rows in building attribute table. Then, the join function was applied between the block attribute table and the building attribute table to add the KBLOCK index field to the block vector.

(d) Finally, the EPC number used in the calculation of the KBLOCK index for each individual block was calculated. Consequently, the information was transferred to the map using the centroids of the polygons corresponding to the blocks.

As explained in the methodology paragraph, the area/size of a building can be used as a weight for calculating the average K-value, and the option to calculate a weighted average K-value was indeed considered in an early stage. A test in a sample portion leading to a difference of less than 5% was discussed with Bologna MA who explicitly required not to weight the K-value, keeping it directly linked to the EPC values of the units included in the block. Even though the EPC availability may not cover all buildings in the block at the moment, Bologna MA and Emilia-Romagna Region are boosting the update of EPCs; therefore, the integration of new certificates will certainly increase the accuracy in the future. Thus the EPC coverage can quickly reach 100% in the coming years, reflecting the situation within the block and the buildings and allowing the MA to monitor the progress in this direction. After a long discussion with the MA about which option would better fit the specific case, the proposed methodology was confirmed.

## *4.4. Validation*

In order to validate the energy maps generated by the described methodology, a sample area was selected to apply the assessment procedure. The site is named "Bolognina" and is made of large blocks, mostly arranged according to a very regular grid of plots, located in the north side of Bologna, near the railway station. As real data on energy consumption are unavailable (typically owned by energy suppliers and strongly covered by data protection regulation), the validation used a new set of EPCs that were provided for this stage only with the full location address (street name and the street number) for each individual building unit. Similar to the previous stage, data are organized in the following separate files: 2009–2011 (full years), 2012–2013 (full years), 2014–2015 (EPCs until 30 September 2015), 2015–2019 (EPCs from the 1 October 2015). Consequently, the following preprocessing steps were needed:

(1) Each EPC dataset was filtered to delete records (almost 4%) including errors (i.e., incorrectly written values in the Eptot field and in address or street number field, etc.)

(2) The EPC datasets were merged into a single file (126,383 records), detecting and erasing EPC duplicates (almost 1%) associated to the same building unit to keep the most updated one.

(3) Eptot values determined before 1 October 2015 for E1 building class were converted to express the unit measure in usable floor area instead of heated volume.

(4) Eptot values (almost 1.5%) over the thresholds of 500 KWh/m2 \* year for residential buildings and 800 KWh/m<sup>2</sup> \* year for nonresidential buildings were excluded, assumed to be out of scale based on regional guidelines.

Then, the validation procedure for the Bolognina sample area was performed, including the following steps:

(1) The specific EPC records were extracted according to Bolognina street locations. The address line was corrected using semi-automatic procedures to make the addresses consistent with those reported in the street number vector file. Address duplications within the street number vector file were deleted to obtain unique values for this field.

(2) The Eptot mean value was calculated starting from EPCs having the same complete address. This step makes the validation comparable with the procedure used in the calculation of the K indicator.

(3) A new one-to-many relationship was created between street number vector (A) and EPC table (B) to match a single record of the street number attribute table (A) with many rows in EPC table. The join function was then applied between the street number attribute table and the EPC table to add the Eptot mean field to the street number vectors.

(4) The Eptot mean value between the street numbers included in the same block was calculated within the upgraded street number vector.

(5) The Eptot mean block value, representing all the street numbers included in the same block, was associated to the block polygons through the attribution for position tool.

## **5. Case Study Results**

The described methodology and related procedures produced vector files containing all the necessary information to represent the estimated energy consumption values on a GIS-based map. Graphical representation can be displayed according to three different scales: buildings, cadastral parcels and blocks. In order to improve map readability, a color palette that varies from green to red—associated to the best and the worst conditions, respectively—was applied. The color scale was divided into six classes from 0–100 KWh/(m2·year) to greater than 300 KWh/(m2·year) (Table 1). The cartographic reference system of all maps is ETRS89/UTM 32N. The original building vector file contained 41,010 buildings and represents the whole city territory at building scale definition.


**Table 1.** Consumption classes according to the energy map visualization.

The uncolored buildings are those not useful for the scope (such as canopies, abandoned or dismissed buildings, kiosks, barracks, etc.) or belonging to specific categories like historical monuments or those not included due to data unavailability or errors. The final file considers 37,651 buildings of which 19,311 (51.3%) are properly classified according to the EPC criteria (Table 2). Figure 3 provides a detailed portion of K index energy map at cadastral parcels scale.

**Table 2.** Summary of resulting data.


**Figure 3.** K index map at cadastral parcel scale. Bing aerial imagery is used as the base map.

Figure 4 offers an overview of the K index map at block scale (KBLOCK index) considering the entire extent of Bologna city. Approximately 12% of the blocks (283) resulted as not being classified. The fourth class (orange 200–250 KWh/(m2·year)) includes the highest number of polygons (960).

**Figure 4.** K index map at block scale (KBLOCK index). Bing aerial imagery is used as the base map.

Figure 5 provides the portion of the map at the block scale corresponding with the sample site used for validation purpose. The same color palette used for the K index maps was applied. For each block, the number of EPCs with complete address, available for the Eptot mean value calculation, is reported.

**Figure 5.** Eptot mean value map corresponding to the validation area (Bolognina) at block scale. Bing aerial imagery is used as the base map.

Figure <sup>6</sup> provides a detailed map of the K index (KWh/(m2·year)) in Bolognina district sample site. The blocks requiring further refinement to be classified through validation procedure are highlighted with a cyan line. The K index classification corresponds in 64 out of 80 blocks, while in the other ones the difference never exceeds one class.

**Figure 6.** The KBLOCK index map corresponding to the validation area (Bolognina) at block scale. Bing aerial imagery is used as the base map.

## **6. Discussion**

The structure and the stages of the proposed methodological approach clearly reflect the constraints regarding the input data availability, particularly the need to properly set the basic unit on which the study focuses. Within the energy efficiency field, this typically refers to the building scale, which may be coherent with the initial request to obtain a picture of the stock that does not meet the minimum energy efficiency requirements but does not fit well with the scope that instead clearly aims to easily visualize and process data about energy consumption distribution at the district/city scales. The building level is a consolidated basic unit adopted both in GIS and building information modeling (BIM) environments for modeling purposes, as clearly evidenced by the literature [66,67]. However, the limitations of EPCs, due to data protection regulations that require treating data without a full address line (street and street number), obliged us to work at cadastral parcel scale and not at the building scale (the ownership of data may differ according to the context, and in other experiences full data were used according to specific agreements). The complete address and the street number code link each cadastral unit with the corresponding vector geometric element [68].

The MA and the research team took the sensitive data issue seriously from the very beginning, assuming that the methodology has to comply with the Legislative Decree 101, 10/08/2018 [36], that acknowledges the European General Data Protection Regulation 2016/679 [69] within the Italian territory. This is the reason why full address data were made available only for validation purpose on a sample area with limited extent, while the methods developed for using nonsensitive data have been instead applied [70,71], complying with the strict nondisclosure agreement for data processing and considering that the generated outcome would display only aggregated data on the map.

The use of EPCs evidenced some other issues concerning the collection methodology and the data storage organization. EPCs were introduced by the Decree Law n.63 (04/06/2013) [72] and following updates, providing a specific template for all new and existing buildings to be rented or sold within the market. In 2015, some new standards were included in the template in order to make the energy performance classification more homogeneous, partially changing the recorded data structure or the data writing formats. Even though this aspect is not part of the present study, it seriously affected the way in which data have to be processed. Some differences were detected in datasets from 2009 to 2015 and those from 2015 to 2018, requiring additional steps for harmonization [73] during the preparation phase for GIS processing. A recurrent detected error regards the address writing format (names and

numbers). The mismatch of the address formulation criteria between the EPC files used for validation and the available vector files obstructs a simultaneous analysis of the entire municipal territory. The frequent typo errors made it impossible to apply an automatic correction procedure, and most of the errors had to be manually amended. This was possible for the sample area (Bolognina), considering its limited extent, but could significantly impact the entire process if applied to the whole city.

As already evidenced by Hohmann [74], these problems suggest a reflection on data collection methods and organization within theMA, especially considering the new three-year plan for information technology in the Public Administration aiming to achieve the digital transformation of the Italian PAs in accordance with the European Digital Agenda programming [75].

Among the detected errors within the EPC files, the one regarding the lack of cadastral data or addresses can be very critical causing the loss of records (which in the specific case study was limited to 5%) and related visualization in the energy map. The lack of geometric congruence between some vector files (such as between building vector and block boundaries vector) can be instead solved by the application of some spatial GIS tools [76].

Taking into account all these limitations, the developed methodology tried to find adequate solutions to generate the energy maps: the K index map was initially obtained at the building scale and then obtained at the cadastral parcel scale, with more than 50% of total geometric units classified.

The number of EPCs used to calculate the K index (referring to the individual cadastral parcel) may vary in each cadastral parcel, consequently the K index of a cadastral parcel can be calculated only as a part of all of the urban units or a part of all buildings included within it. The progressive increase of EPC availability will definitely improve the map reliability until a total coverage is finally achieved. Considering that the available EPCs (covering the time period up to 2018) represent 50% of the urban units (assuming a relevant part of the residual 50% belongs to a building typology that does not require an EPC, as already explained), the developed methodology could be positively refined and implemented during the coming years in order to increase its levels of precision and efficiency.

If the results are observed at the block scale, the K index improves in terms of quality: 88% of the blocks have an associated K-value. Since this scale is more aligned with the planning purposes discussed with the PA, the validation process was performed at the block scale: 80% of the blocks have an associated class corresponding to the K index classification range. Regarding the gap between the two results (conventional methodology and validation), the following considerations can be reported:


Based on the overall outcomes and these observations, the achieved result was considered to be satisfactory by the MA and the team with relation to the initial research brief.

## **7. Conclusions**

Considering all the premises as well as the limitations and barriers that arose during the study with relation to input data, it has to be honestly said that the outcome confirms the constraints and difficulties already mentioned by the scientific literature. However, the result can be considered a step forward in shifting the perspective from the individual building energy performance to a wider urban energy consumption observation, which was the main requirement of the regional regulation update. This will allow the MA to focus on specific measures where energy intensity concentration requires greater attention or to customize solutions with relation to specific situations. It is also very relevant for launching initiatives (such as incentives or bonus) able to boost a diffuse improvement

linked to specific typologies. Much more relevantly, it will be very helpful in monitoring the overall situation detecting where lack of data and information are concentrated as well as in obtaining updated maps which will provide easy-to-visualize feedback of the impact derived by the decisions and energy policies adopted over a time period.

The methodological approach used for generating the K index map can be considered a valid and effective starting point for further detailed elaboration of energy performance maps, taking into account data reliability as an open issue to be further investigated. This can be considered a feasible and realistic goal, assuming that the continuous update of EPCs will make new input data available to feed the methodology, improving its precision and reliability. This will help in taking into account the evolution of energy consumption of buildings over time, keeping the focus on the overall demand and distribution at the city level and supporting reflections on the influence of local and external conditions (such as the effects of climate change). The main benefit deriving from this study lies in the opportunity of using maps to represent a systemic and easy-to-visualize picture of energy consumption at the city level that policy-makers and designers can use to support the decision-making process as well as to easily communicate the outcomes to citizens and end-users, improving the collective level of awareness towards this topic.

It also has to be noted that the described methodology can be generalized and easily replicated in other contexts without its main structure changing, even if the outcomes can be of course affected by the quality of available data and resources. With that being said, this methodology represents an opportunity to explore the energy consumption trends at the city/district scales and consequently address future measures and initiatives to foster energy savings.

**Author Contributions:** Conceptualization, J.G.; methodology, J.G., E.A., M.D.G.; software, M.D.G.; validation, J.G., E.A., M.D.G.; formal analysis, J.G., E.A., M.D.G.; investigation, J.G., E.A., M.D.G., V.V.; resources, J.G., E.A., M.D.G., V.V.; data curation, M.D.G.; writing—original draft preparation, M.D.G.; writing—review and editing, J.G. and E.A.; visualization, M.D.G.; supervision, J.G. and E.A.; project administration, J.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was partially funded by Emilia-Romagna Region, within the framework research program titled "Efficienza energetica in edilizia e nel settore industriale" (G.R. 17147/2017, G.R. 19429/2017) supported by the "Piano triennale integrato Fondo Sociale Europeo (FSE), Fondo Europeo di Sviluppo Regionale (FESR) e Fondo Europeo Agricolo per lo Sviluppo Rurale (FEASR) - Alte Competenze per la ricerca, il trasferimento tecnologico e l'imprenditorialità" funding scheme.

**Acknowledgments:** The authors express their sincere gratitude to V. Orioli, F. Evangelisti, G. Fini, F. Tutino, C. Girotti, C. Manaresi and those who gave their support and their contribution within the Municipality of Bologna.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
