*Article* **PEDRERA. Positive Energy District Renovation Model for Large Scale Actions**

**Paolo Civiero 1,\*, Jordi Pascual 1, Joaquim Arcas Abella 2, Ander Bilbao Figuero <sup>2</sup> and Jaume Salom <sup>1</sup>**


**Abstract:** In this paper, we provide a view of the ongoing PEDRERA project, whose main scope is to design a district simulation model able to set and analyze a reliable prediction of potential business scenarios on large scale retrofitting actions, and to evaluate the overall co-benefits resulting from the renovation process of a cluster of buildings. According to this purpose and to a Positive Energy Districts (PEDs) approach, the model combines systemized data—at both building and district scale—from multiple sources and domains. A sensitive analysis of 200 scenarios provided a quick perception on how results will change once inputs are defined, and how attended results will answer to stakeholders' requirements. In order to enable a clever input analysis and to appraise wide-ranging ranks of Key Performance Indicators (KPIs) suited to each stakeholder and design phase targets, the model is currently under the implementation in the urbanZEB tool's web platform.

**Keywords:** Positive Energy District; smart districts; building performance simulation; sustainable large-scale renovation model; Driving Urban Transition; Renovation Wave

#### **1. Introduction**

The European Commission has set ambitious targets to make Europe the first carbonneutral continent by 2050. As the building sector is one of the largest energy consumers, the European Union (EU) is now stepping up efforts towards citywide transformation to enable transitions towards a climate neutral economy. A refurbished and improved building stock in the EU will help to pave the way for a decarbonized and clean energy system as well as for the development of neutral cities [1]. The improvement of the intervention rate up to 3% per year, which means the need to promote renovation actions, will raise the overall quality of the building stock, especially regarding the energy neutrality, high efficiency and health [2,3]. Indeed, large-scale renovation means also to regenerate and revitalize the social and economic structures locally, and to build trust in the business opportunities and benefits for each actor involved in the process [4]. To pursue these ambitious energy and climate benefits and the economic growth, the Positive Energy Districts (PEDs) approach will be a driving force, bringing the European Green Deal (GD) closer to citizens in an attractive, innovative and human-centered way [3]. The pioneering concept of PEDs, which builds on the paradigm of smart cities, will be incrementally introduced in the energy planning of many cities and communities in the coming years [5]. The Positive Energy District is directly contributing to the Renovation Wave through the strengthening of national innovation policies by coordinating, pooling and increasing of Research and Innovation funding for developing 100 Positive Energy Districts in Europe by 2025. Furthermore, PEDs will help to fulfil the goals set out by international policy frameworks such as the Urban Agenda for the EU, the COP21 Paris Agreement, the Habitat III New Urban Agenda, and the UN's Sustainable Development Goals (notably SDG 11), and to boost the large-scale regeneration of the built environment.

**Citation:** Civiero, P.; Pascual, J.; Arcas Abella, J.; Bilbao Figuero, A.; Salom, J. PEDRERA. Positive Energy District Renovation Model for Large Scale Actions. *Energies* **2021**, *14*, 2833. https://doi.org/10.3390/en14102833

Academic Editor: Alfonso Capozzoli

Received: 26 April 2021 Accepted: 11 May 2021 Published: 14 May 2021

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**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

Aligned with this perspective, the paper describes the ongoing research project "PE-DRERA. Positive Energy District renovation model" whose main goal is to handle several key challenges and sectoral priorities of this urban transitions, by unlocking the potential of business models geared towards large scale refurbishment plans [6]. A model able to provide a reliable prediction of potential scenarios—and their benefits in terms of energy efficiency, well-being and economic topics, among others—in order to plan and execute investment. Indeed, the purpose of the hereunder described PEDRERA project is to create a multidimensional urban building energy modeling (UBEM) [7] tool able to assess and promote the large-scale renovation in the urban areas. According to this objective, the model supports the simulation of different renovation scenarios moving from a set of information that firstly is automatically gathered and/or extracted. The definition of the main input and the calculation of Key Performance Indicators (KPIs) for large-scale renovation strategies are proposed according to a PEDs vision including different stakeholders' perspectives and with special attention to the most vulnerable groups, as well to make the transition perspective, feasible. Once collected, inputs are used to run the PEDRERA model algorithms and suited on each stakeholder involved in the renovation process. In this way, it will promote an action of urban regeneration focused on clear long-term environmental, social and economic objectives [8].

The present paper, oriented to the problem definition and formulation, ready for the implementation, demonstrates how the research addresses several challenges which represent some of the main barriers for this transition process, especially:


Although the potential of available data, some critical barriers could hinder their effectiveness and implementation of the PEDs in the urban environment, and are represented by the ability to aggregate data from different sources and to exploit this information according to specific target groups' "scope". Indeed, the challenge is not only how to gather fair data (reliable, verified and continuous over time), but also how to integrate them in order to formulate predictive and feasible business models adherent to the purpose. One of the key innovation aspects of the PEDRERA model means the way to solve the gaps and the integration from different databases and dispersed information.

According to these addressed challenges, it is well known that the availability of widely monitored and shared data is certainly one of the key aspects of smart and digital cities [9]. Aimed to facilitate collecting, sharing and integrating data about the environment, several initiatives—like the OECD's Open Government Data project and the INSPIRE directive (currently under revision and that will be implemented by 2021)—are meant to create a European Union spatial data infrastructure for the purposes of EU environmental policies [10,11]. In compliance with these initiatives, national land registers—such as the Spanish cadaster—are already sharing their spatial data, including parcels and building characteristics (gross surfaces, number of dwellings and of floors), among others.

On the other hand, in order to address information to specific KPIs and thus explore different scenarios and potentialities allowed, the PEDRERA model adopts the innovative methodology of UBEM developed in the urbanZEB tool provided by the CICLICA, partner of the research project [12,13]. UBEMs have emerged in recent years as efficient hybrid of top-down statistical and bottom-up engineering approaches [14–17] and they are expected to become the main planning tool for energy utilities, municipalities, urban planners and other professionals [18–21]. Both the UBEMs and the virtual city maps are considered as the new generation of tools that based on the digital twins concept, allow the analysis and monitoring of large urban areas and built stock [22–25]. The UBEM allows the multilevel integration of several sources of information, the energy simulation of building and characterization of buildings, and as a result, the generation of new essential knowledge and new scenarios for urban regeneration [26–28].

Two main UBEM approaches can be identified from the literature: physical modelling and data-driven modelling that provide automated generation of building energy models through abstraction of building stock by different "building archetypes", i.e., sample or virtual buildings that characterize subsets of buildings of the same kind [29,30]. With regards to the other existent tools, the joint venture PEDRERA and urbanZEB tool leads to a very dynamic, flexible and definitively accurate data-driven UBEM tool for large-scale prediction. Furthermore, PEDRERA is benchmarking innovative and adaptable refurbishment packages on buildings and supporting the design of successful and effective business models for their large-scale deployment and replication. Based on a quantitative aggregation of data, obtained from different sources (e.g., cadaster, public energy performance certificates, statistical socio-economic local conditions, etc.) the adopted software engine algorithms are already able to both support the calculation of different packages of intervention and provide the simulation of potential scenarios, while dealing with different energy goals (energy efficiency and production), as well costs savings and environmental/welfare co-benefits.

Together these models also enable the analysis of the energy supply and demand in a region; make it possible to develop scenarios; determine a preferred mix of technologies, given certain constraints; simulate behavior of energy producers and consumers in response to prices and other signals, etc.

Nevertheless, based on the lesson learned from similar existent models and software, PEDRERA project aims to build a user-friendly, easy replicable and updatable model that could be useful for energy system transformation and supporting PEDs implementation. For this reason, the number of output (KPIs) handled by the model is reduced to the most relevant information (Table 1). Furthermore, the KPIs are strictly based on each type of actor of the process, thus when specific aspects (e.g., socioeconomic ones) mean the main issue to be considered in the process scenario, prioritization ranking will help to select both the crucial inputs and the drivers to be adopted.

The urbanZEB platform, where PEDRERA model will be implemented, is currently in use and has been adopted to define and support local and national plans and strategies. The results of urbanZEB are accessible through an interactive online platform that allows to consult both the single building level and multiple buildings through mapping, based on graphical data, which include functionalities for comparing scenarios and spatial geographic filtering. The visualization of urban information is in three possible output formats: 3d cartography, database and tables and graphs, while the consultation of urban information is according to three spatial units of analysis: building, census section or neighborhood and municipality. The most relevant experience of implementing urbanZEB so far was the long-term renovation strategy (LTRS) in the Basque Country's building stock, in 2019. The project challenged an innovative action plan which, for the first time on a regional scale, was based on the building-by-building diagnosis of 1.1 million dwellings, providing a significant advancement in the methodology so far employed for large-scale renovation strategies. At metropolitan scale, the urbanZEB tool was implemented in the Barcelona Metropolitan Area, in order to prioritize the intervention of energy renovation in an area of special vulnerability, formed by more than 200,000 dwellings. The urbanZEB tool's calculation engine was also used in the Spanish LTRS 2020 for the energy simulation of the archetypes identified, with the aim of obtaining the energy reduction after the intervention in the national stock.

Once implemented, the PEDRERA tool will facilitate the engagement of multiple stakeholders involved in the building renovation programs to make effective and wellinformed decisions from a cluster of georeferenced buildings. Indeed, the urbanZEB tool is able to simulate each of the sub-models implemented, and where it is possible to choose among many existing simulation engines or tailor-made models, which fit the characteristics of an application case. Hence, the integration and systematization of the information from multiple domains (e.g., building, energy, economy, financing) is the first step to assess and to manage accurately both the complexity of financing renewal processes at district scale and the interests of each stakeholder. The bottom-up approach of the

urbanZEB tool, dealing with the numerical representation of interconnections between the buildings and the surrounding environment, can assess the needs of several stakeholders including the final users' requirement—with a high resolution of outputs. In that case of the PEDRERA model, the accuracy of the simulation engine has been tested and validated to forecast potential scenarios with results that adhere to the market values.

#### **2. Methods**

The methodology deployed in this analysis is summarized in Figure 1. The tool framework is drawn according to a step by step interactive approach where a different deepening of input belongs to each phase of the renovation process: (1) data aggregation, (2) leading phase, (3) demand aggregation, (4–5) concept and technical design, (6) construction, (7) use. As described above, the PEDRERA data-driven model is based firstly on the aggregation of the information system that means to: (1) collect and gather the available data from multiple domains; (2) integrate the available data to create data-driven models and scenarios that enable to gain a better understanding of the complex reality—extended to no-directly related building stock information.

**Figure 1.** Synthesis of the renovation process flowchart and the interactive approach with the PEDRERA model. Source: PEDRERA.

According to the interactive approach scheme presented in Figure 1, the data aggregation from multiple domains and sources (**Step 1**—**data aggregation**) is arranged in a four main domains framework that will be used to assess the renovation program: business model, environmental, operative and social issues (*tags*).

Indicators to evaluate the status renovation potential of a particular urban area are supposed to capture and process fair data from multiple sources: energy performance, census, cadaster, building physical characteristics, etc. Each input means an indicator that will be introduced in the algorithms for the calculation of the different KPIs according to the scopes:


Moreover, socio-economic indicators such as low income and aging population are also observed in order to cluster most intensely certain urban environments as representative of social cohesion as well as useful to design the business models and to evaluate their impact on each scenario. Since they are significantly representative of household vulnerability, they are likewise considered as input of the risk poverty for both the sustainability of the business model, and the renovation strategy to adopt. That information brings together the first collection of input that will be used as basis of knowledge and for the design of the renovation scenarios. For this scope, the different types of sources are defined and listed. Otherwise, inputs are imported, calculated and/or simulated from statistical information for single customized projects.

The aggregated data are systemized according to the sub-categories and scopes of the PEDRERA framework, as described later. All the information is georeferenced to the single buildings according to the above mentioned four "tags" (Figure 2) that make quick and clever the selection and the assessment of potential buildings to be renovated, as well as taking into account the engagement of the stakeholders (agent/user) involved in the process (**Step 2**—**leading**) (Figure 3).

**Figure 2.** Multidimensional model indicators (layers) displayed in the platform and supporting residential building retrofitting programs. Source: authors.

**Figure 3.** Extract of the PEDRERA model approach expected visualization in the urbanZEB tool: (**a**) Data aggregation at urban level; (**b**) leading phase and stakeholder engagement for the cluster of building selection. Source: authors.

> Input data are supposed to be automatically gathered from a large number of external data sources parameters (e.g., technical and architectural aspects, weather, demographic/aging condition, income/energy poverty indicators or socio-economic rating, energy demand and consumption data, etc.). Multiple sources of data are available for this scope (open data) and can be freely used, shared and built-on by anyone, anywhere, for any purpose, although each one is managed by a different organization, and collected for a specific purpose (e.g., improving building energy efficiency, evaluating the status of the building stock, etc.). In addition to gathering and integrating the aggregated data, the PEDRERA model algorithm exploits the potential relationships between data to design each renovation measure and to evaluate their impact on all the above listed scopes (**Step 3**—**demand aggregation**) (Figure 4).

**Figure 4.** Extract of the PEDRERA model approach, expected visualization in the urbanZEB tool. Demand aggregation menu with: (**a**) the renovation measures input; (**b**) the integration of aggregated and gathered data for the cost analysis. Source: authors.

> Once inputs are collected, systemized and filtered, the model provides the calculation of composite performance output (KPIs), as well as the information on compliance with the targets established by the stakeholders. Both the value and the prioritization rank of each input (attributes and variables to be included in the algorithms) and output KPIs

will modify the renovation strategy to be adopted and, consequently, the scenarios results (**Step 4**—**concept design**). The forecasted scenarios displayed do reflect almost a range of the potential menus of intervention and, thanks to their friendly visualization, will help the exploitation of the results obtained for the next design phases (**Steps 5, 6 and 7 of the renovation process**), integrating the stakeholders' requirement and expectations (Figure 5). After the conclusion of the renovation process, the same platform will also enable to update data stored in the repository for future analysis and intervention.
