Heating Energy Performance Gap in Vulnerable Households: Identification and Impact of Associated Variables

Abstract

Reducing energy consumption in the construction sector is urgently needed. In Chile, where income distribution is unequal and the cost of energy is high, energy demand is seriously affected, especially in vulnerable households. Hence, it is essential to establish public policies with more realistic energy-saving goals to address this situation. However, reliably predicting the energy performance of buildings remains a challenge. For this reason, this study aims to identify and evaluate the impact of the variables associated with energy performance in vulnerable households in Central-Southern Chile and propose values that would reduce the gap. A sensitivity analysis was conducted to achieve this, adjusting the energy performance parameters in a base model with data analyzed using local standards. In addition, field information was collected in 93 households to obtain the actual energy consumption. The main results show that the variables that most impacted performance were infiltration, COP, heating setpoints, and schedules, which generated a 60% difference between the theoretical and actual consumption.

1. Introduction

Numerous studies have revealed that reducing energy consumption is urgent due to the impact of climate change [1]. In 2016, the construction sector accounted for almost 40% of total global energy consumption and 33% of total greenhouse gas (GHG) emissions, figures that would be even higher if the entire life cycle of buildings were considered [2]. That is why policies, programs, and campaigns have highlighted the importance of including energy consumption as an essential need or right, since energy and well-being are closely interconnected [3].

Several factors, such as location, envelope characteristics, internal loads, and HVAC equipment, must be considered to quantify energy consumption in buildings and address thermal comfort requirements [4]. In this sense, simulation tools play a crucial role since they allow the study and efficient prediction of the energy performance of buildings during the design stage [5]. A well-developed simulation model can offer accurate predictions considering the complex interactions and physical relationships [6].

Although dynamic building simulation tools are widely recognized as an appropriate way to assess building performance and develop energy efficiency (EE) policies, significant discrepancies are still observed between the simulated characteristics and reality, both in new and existing buildings [7]. These discrepancies are known as the energy performance gap (EPG), which is defined as the difference between expected (estimated, calculated, predicted) energy consumption and actual construction-related consumption [8].

The literature identifies different causes that contribute to these discrepancies that can be grouped into three main categories: design stage causes, construction stage causes (including delivery), and operational stage causes [9]. One key factor in the EPG at the design stage is energy modeling and building simulations [10]. Although a trained analyst can correctly apply a model, the predictions are still subject to fundamental uncertainties, such as the actual weather conditions, the occupancy profile, the effectiveness of solar control, and the distribution of internal heat gain, to highlight some [11].

Other complexities related to control strategies, poor construction practices, improper commissioning, and contractors’ lack of participation in the adjustment and refinement of buildings after completion contribute to the EPG in the construction stage [12]. Often, measurements are the only way to reveal problems, although in many cases, visual inspections are required to identify the real problems [13].

At the operational stage, the presence of occupants and their interaction with the different components of the building significantly impact the energy consumption predictions made using simulations, even if the weather conditions, the building envelope, and the equipment are well-defined [14]. The lack of routine processes such as post-occupation evaluation (POE) means that there is no mechanism to improve the building’s energy performance at this stage [15]. One line being pursued is using system-level measurements to obtain more detailed information. This could take the form of separate measurement approaches for the building structure, building services, and occupants [16]. However, it is difficult to translate these data into input information for computational predictions that reduce the EPG.

Therefore, it is vital to have a solid knowledge base on how occupants use energy to improve simulations and the effectiveness of saving campaigns [17]. The specific context where homes are located is determined by various factors that can influence energy behavior and, in turn, the effectiveness of interventions [18]. People are strongly influenced by their environment, which shapes their behavior (sometimes highlighting cognitive biases), cultural models, and social practices [19].

Compared to simulations based on regulatory standards, differences in the actual behavior in housing can lead to a significant overestimation of HVAC consumption and energy-saving potential. Some authors have described this phenomenon as the “rebound effect” and the “pre-bound effect” to explain the energy performance gap [20]. The “rebound effect” occurs when improvements in energy efficiency reduce the marginal cost of energy services. This encourages higher service consumption and can partially or totally compensate for the reduction in energy consumption. On the other hand, the “pre-bound effect” refers to the low consumption of energy services in old and inefficient homes before or in the absence of energy rehabilitation, resulting in a lower-than-expected energy-saving potential [21]. Some of the research on climates with heating requirements links these effects with the economic capacity of families, demonstrating that consumption below theoretical levels is partly explained by matters related to poverty and preferences for energy savings over comfort. Meanwhile, households that consume more than expected prefer comfort, have higher incomes, or more efficient homes. These highlight that classifying energy consumption patterns in the residential sphere can help understand energy performance gaps and adjust public policies to the different profiles [22].

Some international standards, such as China’s, use the full-time full-space method to calculate the energy consumption of residential buildings. This method assumes that the internal heat gains of the occupants and appliances are constant and do not change with time or space and that the air conditioning system works 24 h a day, seven days a week, and for all rooms [23]. This method can effectively calculate the energy consumption of a country’s houses. However, to more reliably determine energy consumption, it is necessary to evaluate which parameters are the most appropriate for each social reality.

In Chile’s specific case, the heating demands in social housing have evolved in direct relation to the thermal requirements of housing projects established in recent years through the Thermal Regulation (TR) [24]. This regulation, in force since 2007, considers insulation in roofing, walls, ventilated floors, and windows as pretty basic standards that have helped the industry to gradually introduce components of the envelope that improve the building’s energy efficiency and mitigate pathologies and thermal discomfort [25].

To improve these conditions and control air pollution, the authorities have implemented Atmospheric Decontamination Plans (PDA, in Spanish), which provide families with programs and subsidies to improve the quality of firewood, replace old wood burners, and encourage the modernization of homes [26]. Although these energy modernization plans, in force since 2014, have advanced EE for housing, they have only been applied in the areas declared as GHG-saturated, disregarding other affected communes. In addition, the Energy Efficiency Law has been promoted, which came into force in the first semester of 2023 and will oblige construction and real estate companies, as well as housing and urban planning services, to use the Energy Rating (CEV, in Spanish) in all new projects, initially in housing [25].

The Energy Rating is directly related to the Sustainable Construction Standard for Housing (ECSV, in Spanish), although its application is still voluntary. This standard seeks to provide guidelines on energy efficiency for the construction sector, following the government’s energy policies, such as the “Chile Energy Policy 2050” published by the Ministry of Energy in 2015. Its objective is to establish standards and best practices for the design, construction, and operation of new and used homes to improve their environmental, economic, and social performance by incorporating sustainability criteria based on objective and verifiable parameters [27].

However, these objective parameters do not necessarily fit the reality of buildings and their occupants, and they should be measured to determine the possible impact they could generate, especially in the homes of families with fewer resources, as has happened in some EU governments with the application of the Energy Performance Certificate (EPC) [28]. For example, in homes in Central-Southern Chile, it has been identified that vulnerable households regulate the use of heating, obtaining indoor environments whose temperatures are below international standards [29]. Digital thermal behavior simulations have helped to clear doubts about how the different architectural design parameters affect a project’s energy performance [30]. Nonetheless, evaluating this performance is usually a multidimensional phenomenon, which requires a multifaceted approach containing different and effective performance indicators according to the measured performance [31].

Table 1. Survey of variables that impact energy consumption, according to the literature.

			Variables
Source	Country	Köppen–Geiger Climate Classification	Climate	Envelope	Systems and Equipment	Operation and Maintenance	User Behavior	Indoor Environmental Quality
[24]	Chile	-	●	●	●	●	●	●
[32]	Chile	-	●		●	●	●	●
[17]	Netherlands	Cfb	●	●	●		●
[33]	Chile	Csb	●	●	●
[34]	-	-	●	●	●	●	●	●
[1]	-	-	●	●	●	●	●	●
[6]	United Kingdom	Cfb		●	●	●	●
[23]	-	-	●	●	●	●	●	●
[35]	Uruguay	Cfa	●	●		●	●
[28]	Spain	Cfb	●	●	●	●	●	●
[36]	Canada	Dfc	●		●	●
[20]	Poland	Cfb	●	●	●	●	●	●

presents the list of variables that impact the energy consumption of the house identified in [1], which covers the different stages of housing: design, construction, and operation. It is summarized in addition to the identification that other authors make to these variables, from which it follows that the climate, the envelope, systems and equipment, and user behavior are the factors mostly identified, followed by operation and maintenance, and finally, the quality of the indoor environment (understood as the range of comfort temperatures). Therefore, these variables should be paid attention to when predicting energy consumption to fit the population studied as closely as possible, thus reducing the energy performance gap.

Despite this, the variables associated with the heating energy performance gap in vulnerable households have not yet been identified. Hence, this research aims to identify and measure the impact of the variables associated with energy performance in the homes of vulnerable families in Central-Southern Chile and propose values that allow reducing the energy performance gap below 10%. The parameters proposed in the local standards will be compared with the values obtained from the fieldwork, which included the study of 93 vulnerable households. In this way, knowledge and public policies will contribute to making more effective decisions in the early stages of the project based on more reliable information and assumptions through more accurate and detailed simulations.

2. Methodology

A non-experimental correlational quantitative methodology was used to obtain data through fieldwork in Central-Southern Chile. Before collecting the information, a survey of the current variables used in energy simulations for homes in the country was conducted. The references, norms, and standards currently used in home evaluations were reviewed to identify the most relevant parameters and their values.

Subsequently, information was collected through surveys to study the impact of the variables on energy performance, focusing on the user, the constructive characteristics of the house, energy consumption, and usage profiles.

Finally, a case study was chosen for evaluation through an analysis. Systematic changes were made in the input parameters of a model to determine the effects of these modifications on the energy performance gap, and finally, the variables that had the most significant impact and their recommended values were defined (see Figure 1).

2.1. Identification of Relevant Parameters

The variables were identified based on a literary review, a digital search of scientific articles, and analysis categories to gather information on energy consumption in housing, the energy performance gap, energy poverty, energy modeling of buildings, and others. Inclusion and exclusion criteria, such as databases, periods, and most cited articles, were defined. Then, truncations were established with the keywords, and a search was performed, obtaining an Nn number of articles. These articles were exported to the VOSviewer 1.6.20 bibliometric visualization software, which filtered the articles by citations, documents, and relationships between authors. Finally, these were saved and classified, together with the other studies, in the Mendeley reference management 2.122.0 program.

After that, the current regulations in Chile associated with the energy efficiency of housing (Thermal Regulation—2007) and those still voluntary but projected as future solutions in the residential sector (Sustainable Construction Standard for Housing and the 2018 Housing Energy Rating) were reviewed.

However, to organize this research, it was decided to evaluate the ECSV as a comparison standard because it is the only local standard that considers energy simulation within its evaluation parameters, in addition to estimating and improving all the energy conditions proposed by the current TR.

The EE standards for designing and building homes and energy performance goals for their operation are established in the ESCV’s “Energy” category. Despite this, particular emphasis was placed on analyzing the design stage to determine the values established in the different variables when performing energy simulations, reviewing all the objective and verifiable parameters proposed by the Standard for Thermal Zone F (Interior), the zone of the commune under study. This is a wet and cold area with regular rain, short 4-to-5-month summers with moderate sunshine, numerous lakes and rivers, microclimates, robust vegetation, and moist soil with south and southwest winds. According to the 5 kWh/m²-year standard, almost zero cooling energy is needed. Hence, heating energy consumption (HEC) was adopted as a variable. This is one of the peculiarities of the research because they are homes that are heated mainly with firewood, as determined in the surveys carried out.

After reviewing the state of the art and considering the different points of view to more reliably address the representation of energy consumption in the design stage through simulation and analyzing the local regulatory standards as a base source of comparison, the independent variables were determined in the results phase, as described in

Table 2. Variables associated with energy performance.

Category	Variable	Unit
Envelope	Thermal transmittance of Walls	W/m2-K
	Thermal transmittance of Roof	W/m2-K
	Thermal transmittance of Floor/Slab	W/m2-K
	Thermal transmittance of Glazing	W/m2-K
	Thermal transmittance of Doors	W/m2-K
	Infiltrations	ac/h at 50 Pa
Climate	Climate File	-
	Ground Temperature	°C
Indoor environmental quality	Heating and Cooling Limit	°C
	Natural Ventilation	ACH
Systems and equipment	Performance coefficient	COP
	Internal lighting load	W/m2
Occupant behavior	Lighting occupancy schedule	Hours per day
	Number of people	People
	Heating act. hours	Hours per year
	Cooling act. hours	Hours per year
	Natural ventilation schedule	Hours per day
	Metabolic Index	W/person
	Internal loads schedule	Hours per day

.

2.2. Selection and Characterization of the Case Study

2.2.1. Determination of the Case Study

The place of study of the research was the commune of Mulchén, located in the Biobío Region, Chile. This city was chosen because it is part of a public policy program promoting energy efficiency in vulnerable families’ homes through the Ministry of Energy’s With Good Energy Program (PCBE, or Programa Con Buena Energía). According to the 2006 CASEN Survey, it is above the regional poverty average but has worrisome levels of atmospheric pollution at a communal level.

2.2.2. Sample

A non-probabilistic sampling was defined as a group of users who benefited from the PCBE in the commune of Mulchén in 2018 because they were considered vulnerable households. This list is for 147 families, which are defined as those households that are characterized in the Social Registry of Households (RSH, in Spanish) with a Socioeconomic Qualification (CSE, in Spanish) of Segment 1, which means that they belong to the 40% most economically vulnerable households in Chile [37]. Ninety-three homes in a nearby radius agreed to participate in the study between July and September 2022, thus obtaining representative data on occupant behavior during regular hours of use and energy consumption for heating.

2.2.3. Survey

Data collection was carried out using a survey hosted on a website. The survey, consisting of 156 questions, seeks socioeconomic information and characterizes families’ housing, consumption, and energy behavior. The use of an online platform helped systematize the information and maintain a record of the dates and times when the surveys were taken and the time of application. The preparation of a consent form signed by the participants was considered ethical.

The complete survey considered 11 information sections, of which 7 were considered for the section of questions associated with the housing’s energy performance variables, which are not necessarily in a correlative order. The first section deals with household information, the next two with the physical characteristics of the house, then a section on energy, heating, and cooling, before ending with indoor temperature, comfort, and lighting, for a total of 27 questions (see Appendix A).

A team of three people, including the coordinator, collected data in person between July and September 2022. The survey takers were previously trained regarding the study’s objectives, protocols, and instruments to be applied. They also had experience conducting surveys, allowing them to control possible biases.

Before the fieldwork, contact was made with the PCBE regional representative of the Ministry of Energy to coordinate jointly with the Municipality of Mulchén through the SECPLAN meetings with the Neighborhood Boards. The intention was to resume contact with the beneficiaries of 2018 and present them with the study. With municipal help, it was possible to contact the 147 families by telephone and inform them about the investigation and the subsequent visit of the survey takers. Finally, the latter coordinated the application of the surveys with 93 families from the state program, who agreed to provide data voluntarily, confidentially, and free of charge to conduct this study.

2.3. Definition of Values for Variables

The values adopted for the variables came from different sources depending on the evaluated case. The way in which the data were obtained for each case study is outlined below according to the type of variable studied.

Base Case. In this case study, the independent variables of the design stage were evaluated, and the data were defined from the local regulatory standards according to the architectural typology of research and the geographical context.

Built Case. The definition of the values of the independent variables in this case, which is for the operation stage, was mainly achieved through the answers raised in the surveys, which were statistically classified using filters and calculation formulas in Excel. However, the value of some parameters could not be collected onsite due to the complexity and associated costs. These include infiltrations, natural ventilation, equipment performance coefficient, metabolic index, and schedule of internal loads. Hence, these were determined by analyzing manuals and standards and through local investigation.

2.4. Real Case

To obtain the value of the heating consumption dependent variable for the operation stage, a calculation was made using the most representative type of combustion of the 93 houses built (Q96), the means of fuel expenditure (Q53 and 67), the most active average months (Q97), and the average usage time (Q98). Subsequently, a sales value of 1 m³ of firewood, representative of the area, was obtained through the report of [38]. Conversions of the requested units were then made, adding the value of the energy contained in 1 m³ of firewood with a humidity of 40%, a representative percentage of the area according to [39], using a biomass calculator generated by [40], concluding in a heating energy consumption in kWh/year.

In the next stage, the research’s initial energy design model was made based on the architectural design of the houses seen onsite. This was done using DesignBuilder (DB) Version 6.1.0.006, which uses the EnergyPlus 23.1.0 calculation engine simulation software.

Base Case Modeling

The first parameters used as a basis of the case study were the architectural characteristics and materiality identified in the survey’s Q31–35, obtaining an isolated dwelling (Q25) of one floor (Q26) with a wooden structure (Q31) with an area between 50 and 100 m² (Q27). This architectural typology was identified as a Base Case and served as an example in its form for all simulated cases.

The selected typology consisted of 3 bedrooms, 2 bathrooms, a living room, and a kitchen, with a resulting area of 58.8 m², according to the most repeated conditions of the field study seen in questions Q25–27. This resulted in the design presented in Figure 2.

Once the modeling was reviewed in the simulation program, the variables defined according to each case’s objective data detailed in Table 2 and according to the architectural conditions of the proposed typology and the studied environment were calibrated. The introduction of these values was carried out systematically in the different sections of the program and, according to each parameter, grouped into the following sections: Site, Activity, Enclosures, Openings, Lighting, and HVAC, ending with the consumption results shown in the Simulation section using the EnergyPlus 23.1.0 calculation engine.

3. Results

3.1. Characterization of Variables

The first result of the analysis of parameters considered for calculating the energy performance of houses according to the standards is shown in Table 2, using the variables grouped into categories presented in the literature study collated in Table 1.

The envelope elements’ thermal transmittance values were determined from the survey of the constructive systems of the case studies and the calculation of their thermal transmittance (see

Table 3. Thermal transmittance (U-value) of the envelope variable.

Element	Int/Ext Layers	Thickness	Unit	U-Value	Unit
Walls	Plasterboard cardboard	10.0	mm	3.4	W/m2-K
	OSB board	9.5	mm
	Wooden decking	2.5	mm
Roof	Plasterboard cardboard	10.0	mm	4.1	W/m2-K
	OSB board	9.5	mm
Floor/Slab	Wooden floor	2.5	mm	1.5	W/m2-K
	Concrete base	80.0	mm
	Sand	100.0	mm
Glazing	Generic single glazing	3.0	mm	5.9	W/m2-K
Doors	Wood	45.0	mm	2.5	W/m2-K

). These values are considered because they are higher than those established by the ECSV, and their impact on energy consumption is already known. Therefore, this variable is discarded, using the same U values in both the Base and Built Cases.

For the natural ventilation, metabolic index, and internal loads schedule variables, the values assigned in the standards (see

Table 4. Values of the matching variables for both case studies.

Category	Variable	Unit	Data
Indoor environmental quality	Natural Ventilation	ACH	3.0
Occupant behavior	Cooling act. schedule	Hours per year	0.0
	Metabolic Index	W/person	98.4
	Internal loads schedule	Hours per day	24.0

) were kept for both the Base and Built Cases due to the complexity and cost of collating field data. On the other hand, it was decided to omit the cooling activation schedule due to the limited use stated in the survey questions Q105, 107, and 108 and the low value given in ECSV for the study area.

In fact, the 10 variables accepted for measuring the impact on HEC were characterized for each case study using Table 5, following the methodology described and what was concluded above. The Base Case uses what was analyzed in the local regulatory standards, and the Built Case uses what was collected onsite together with the analysis of manuals and local investigations.

Table 5. Characterization of variables.

	Variable	Base Case	Built Case
V01	Infiltrations	The maximum air infiltration class allowed for the thermal envelope of buildings in the Biobío region is 8.00 ac/h at 50 Pa.	The surveys conducted in the study showed half-timbered houses as the most representative case, whose representative value is 24.6 ac/h at 50 Pa [41].
V02	Climate file	Climate file with Energy Plus Weather format (epw), available for free on the csustentable.minvu.gob.cl website. There were only 20 files for the whole country on the platform, so the one closest to the study area is used, which is Concepcion-Estaciones.epw.	The data from the 2020 Human Weather Station were used. This is the climate source closest to the commune of Mulchén, which is 25 km from the city center.
V03	Ground Temperature	This variable is not considered in the Standard.	This variable was included because it contributed to the simulations and, according to the study of [34], approximated the realities of the environment. Although these values could not be provided in the field, the ground temperature was validated in the model with the Ground Domain (GD) tool in DB and is associated with the temperature of the previous variable. This considers the gravel-based ground characteristic of the area, with a conductivity of 0.5 W/m K, a heat capacity of 184.0 J/kg-K, a density of 2050.0 kg/m3, a ground domain depth of 10 m, and an incidence perimeter of 5 m.
V04	Air-conditioning setpoints	90% acceptability of the ASHRAE 55 adaptive thermal comfort model [42], calibrated with monthly temperatures.	80% acceptability of the ASHRAE 55 adaptive thermal comfort model [42], calibrated with daily temperatures.
V05	COP	ECSV does not include wood-fired equipment specifying that the performance must be greater than or equal to 100%.	The performance coefficient of the typical heaters used by the studied households was adopted following questions Q91 and Q93, a double-chambered wood-burner, supported by the study of [39], with an efficiency of 70%, according to the Bosca Wood-burning Heaters User Manual.
V06	Lighting load	1.5 W/m2.	The data for this variable were obtained thanks to the questions asked in Appendix A, where the average of question Q119 was measured, resulting in a power of 3.7 W/m2.
V07	Lighting usage schedule	January–February/November–December 21–22 h. March–April/September–October 7–8 h/20–22 h. May–August 7–8 h/18–22 h.	The lighting activation schedule stated by the inhabitants of the surveyed dwellings established two and a half hours of use 365 days a year, according to the average from question Q120 in Appendix A.
V08	Number of people	They will be used with an occupation following NCh 3308:2013, which determines a minimum of two people, plus one for each bedroom.	According to the surveys, the average number of inhabitants per dwelling is three people, as per question Q14 in Appendix A.
V09	Air-conditioning schedule	The thermal demand is calculated to permanently reach the comfort levels 24/7, regardless of the house’s actual use schedule.	The schedule profile is summarized in the number of hours stated in the surveys and the exact time of turning on during the day according to the study’s Week CM profile (weekday in a cold month) [37], with the period detailed in Table 6.
V10	Natural Ventilation	Between 10 p.m. and 6 a.m. and when the outside temperature exceeds 15 °C (8 h).	Two hours a day are stated, following the mode arising from questions Q50–52.

Table 5. Characterization of variables.

	Variable	Base Case	Built Case
V01	Infiltrations	The maximum air infiltration class allowed for the thermal envelope of buildings in the Biobío region is 8.00 ac/h at 50 Pa.	The surveys conducted in the study showed half-timbered houses as the most representative case, whose representative value is 24.6 ac/h at 50 Pa [41].
V02	Climate file	Climate file with Energy Plus Weather format (epw), available for free on the csustentable.minvu.gob.cl website. There were only 20 files for the whole country on the platform, so the one closest to the study area is used, which is Concepcion-Estaciones.epw.	The data from the 2020 Human Weather Station were used. This is the climate source closest to the commune of Mulchén, which is 25 km from the city center.
V03	Ground Temperature	This variable is not considered in the Standard.	This variable was included because it contributed to the simulations and, according to the study of [34], approximated the realities of the environment. Although these values could not be provided in the field, the ground temperature was validated in the model with the Ground Domain (GD) tool in DB and is associated with the temperature of the previous variable. This considers the gravel-based ground characteristic of the area, with a conductivity of 0.5 W/m K, a heat capacity of 184.0 J/kg-K, a density of 2050.0 kg/m3, a ground domain depth of 10 m, and an incidence perimeter of 5 m.
V04	Air-conditioning setpoints	90% acceptability of the ASHRAE 55 adaptive thermal comfort model [42], calibrated with monthly temperatures.	80% acceptability of the ASHRAE 55 adaptive thermal comfort model [42], calibrated with daily temperatures.
V05	COP	ECSV does not include wood-fired equipment specifying that the performance must be greater than or equal to 100%.	The performance coefficient of the typical heaters used by the studied households was adopted following questions Q91 and Q93, a double-chambered wood-burner, supported by the study of [39], with an efficiency of 70%, according to the Bosca Wood-burning Heaters User Manual.
V06	Lighting load	1.5 W/m2.	The data for this variable were obtained thanks to the questions asked in Appendix A, where the average of question Q119 was measured, resulting in a power of 3.7 W/m2.
V07	Lighting usage schedule	January–February/November–December 21–22 h. March–April/September–October 7–8 h/20–22 h. May–August 7–8 h/18–22 h.	The lighting activation schedule stated by the inhabitants of the surveyed dwellings established two and a half hours of use 365 days a year, according to the average from question Q120 in Appendix A.
V08	Number of people	They will be used with an occupation following NCh 3308:2013, which determines a minimum of two people, plus one for each bedroom.	According to the surveys, the average number of inhabitants per dwelling is three people, as per question Q14 in Appendix A.
V09	Air-conditioning schedule	The thermal demand is calculated to permanently reach the comfort levels 24/7, regardless of the house’s actual use schedule.	The schedule profile is summarized in the number of hours stated in the surveys and the exact time of turning on during the day according to the study’s Week CM profile (weekday in a cold month) [37], with the period detailed in Table 6.
V10	Natural Ventilation	Between 10 p.m. and 6 a.m. and when the outside temperature exceeds 15 °C (8 h).	Two hours a day are stated, following the mode arising from questions Q50–52.

Table 6. Heating activation schedule according to the surveys and study.

Schedule from–to
	March–April	May–August	September	October
	GMT-3	GMT-4	GMT-3	GMT-3
Surveys	6–9/17–23 h	6–9/17–24 h	6–9/17–23 h	6–9/17–24 h

Note (1). The activation time indicated matches the number of hours stated in the surveys.

Table 6. Heating activation schedule according to the surveys and study.

Schedule from–to
	March–April	May–August	September	October
	GMT-3	GMT-4	GMT-3	GMT-3
Surveys	6–9/17–23 h	6–9/17–24 h	6–9/17–23 h	6–9/17–24 h

Note (1). The activation time indicated matches the number of hours stated in the surveys.

Table 7. Calculation of installed lighting power—Built Case.

Room	Number of Lights	LED Bulb Power W/h	Total W/h
Master Bedroom	2	20	40
Bedroom 2	1	20	20
Bedroom 3	1	20	20
Bathroom 1	1	20	20
Bathroom 2	1	20	20
Kitchen	1	20	20
Dining Room	1	20	20
Living Room	2	20	40
Hallway	1	20	20
Total	11		220
Total surface area—Standard dwelling (m2)			59.0
Installed lighting power (W/m2)			3.7

Note (1). Rooms as per Standard Dwelling. Note (2). Surface area of Standard Dwelling. Note (3). LED bulb wattage obtained from table Consumption of electrical appliances in the home—Ministry of Energy.

is presented as a complement to the lighting load variable for the Built Case.

According to what is indicated in the air-conditioning schedule variable for the Built Case, Table 6 shows the schedules of the survey, which is similar to the time range proposed by [43] as being representative of the heating usage schedule in this area of Chile, as shown in Figure 3.

Finally, the natural ventilation activation schedule used for the Constructed Case is specified in

Table 8. Natural ventilation activation schedule.

Schedule from–to
	January–March	April	May–August	September	October–December
	GMT-3	GMT-3	GMT-4	GMT-3	GMT-3
Surveys	8–10 h/16–19 h	8–9 h	8–9 h	8–9 h	8–10 h/16–19 h

Note (1). The proposed activation schedule matches the number of hours stated in the surveys.

.

3.2. Simulation of Variables

Having identified the variables associated with the dwelling’s energy performance and the values for the Base Case and Built Case, it was necessary to analyze how they impacted each one of these variables and thus know the dimensions individually. However, it was important to know beforehand the value of the variable dependent on the heating energy consumption of the studied case.

3.2.1. Dependent Variable, Heating Energy Consumption

The heating energy consumption resulting from the surveys is a monthly expenditure of CLP 40,000 in firewood between March and October, with a lighting time of almost 9 h a day (see Table 6). The actual consumption was calculated with these data, plus other values provided by the state of the art.

Following the calculation of

Table 9. Calculation of annual heating energy consumption—Real Case.

	Monthly Cost of Firewood for Heating (CLP)	Months Device Is Turned on	Sales Price 1 m3 (CLP)	Monthly Firewood Consumption (m3)	Annual Firewood Consumption (m3)	Energy Contained in 1 m3 of Wood with 40% Humidity (kWh)	Heating Energy Consumption (kWh/year)
Value	40,000	March–October	50,000	0.8	6.4	1155.0	7392.0
Source	Surveys	Surveys	Araucanía firewood report	Calculation	Calculation	Ministry of Energy Biomass Calculator	Calculation

, the annual heating energy consumption for the Real Case is 7392.0 kWh/year. This value is supported by the average consumption of firewood energy in households in the Biobío region, which is 7.138 kWh/year according to [39] and 7.216 kWh/year from the research of [44].

3.2.2. Independent Variables

Before concluding the comparative study of the variables’ impact on the HEC, the differences in each of the ten variables alone were analyzed between the Base and Built Cases, as shown in

Table 10. Comparison of variables associated with the performance gap, Base Case vs. Built Case.

	Category	Variable	Unit	Base Case Data	Built Case Data	Difference
V1	Envelope	Infiltrations	ac/h at 50 Pa	8.0	24.6	16.6
V2	Climate	Climate File	°C	Tmin. 5.1–Tmax. 22.2	Tmin. 3.8–Tmax. 29.3	Tmin. 1.3–Tmax. 7.0
V3		Ground Temperature	°C	-	Tmin. 3.8–Tmax. 29.3	Tmin. 3.8–Tmax. 29.3
V4	Indoor environmental quality	Heating and Cooling Limit	°C	Monthly T° based on ASHRAE adaptive comfort, +2.5 °C to −2.5 °C band (90%)	Daily T° based on adaptive comfort, +3.5 °C to −3.5 °C band (80%)	-
V5	Systems and equipment	Performance coefficient	COP	1.0	0.7	0.3
V6		Internal lighting load	W/m2	1.5	3.7	2.2
V7	Occupant behavior	Lighting occupancy schedule	Hours per day	3.0	2.5	−0.5
V8		Number of people	People	5.0	3.0	−2.0
V9		Heating act. hours	Hours per year	8760	2359	−6401.0
V10		Natural ventilation schedule	Hours per day	8	3	5.0

.

It was important to compare the values before the simulation and to obtain a first impression of the differences between the Base and Built Cases. One of them, for example, was the significant disparity, even above 7 degrees, in the maximum temperatures between the climate files. Other values that stand out were the infiltrations with differences of 16 ac/h at 50 Pa, the hours of use and the heating setpoints directly related to occupant behavior, and the equipment performance coefficient.

3.3. Measuring the Impact of Variables and the Heating Energy Performance Gap

Finally, the impact of the variables and the heating energy performance gap were measured. For this, the annual heating consumption of the Real Case was used as a basis, whose value was calculated in Table 9 and associated only with the monthly monetary expenditure of families on fuel. The variables’ simulations were adjusted with the values of Table 5.

As summarized in

Table 11. Measurement of the impact of the variables and the heating energy performance gap between the Base Case, Built Case, and Real Case.

	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	Consumption (kWh/m2-Year)	Consumption (kWh/Year)	Difference R (%)	Difference B (%)
Real	-	-	-	-	-	-	-	-	-	-	125.7	7392		−38%
Base-Simulated	8.0	E	E	90%	100%	1.5	3	5	8760	8	202.0	11,878	+61%
Adjustment 1	24.6	E	E	90%	100%	1.5	3	5	8760	8	251.9	14,812	+100%	+25%
Adjustment 2	8.0	R	E	90%	100%	1.5	3	5	8760	8	224.9	13,226	+79%	+11%
Adjustment 3	8.0	E	R*	90%	100%	1.5	3	5	8760	8	164.9	9696	+31%	−18%
Adjustment 4	8.0	E	E	80%	100%	1.5	3	5	8760	8	160.0	9409	+27%	−21%
Adjustment 5.1	8.0	E	E	90%	70%	1.5	3	5	8760	8	282.3	16,599	+125%	+40%
Adjustment 5.2	8.0	E	E	90%	80%	1.5	3	5	8760	8	248.9	14,632	+98%	+23%
Adjustment 6	8.0	E	E	90%	100%	3.7	2.5	5	8760	8	201.9	11,873	+61%	+0%
Adjustment 7	8.0	E	E	90%	100%	1.5	3	3	8760	8	216.2	12,711	+72%	+7%
Adjustment 8	8.0	E	E	90%	100%	1.5	3	5	2359	3	118.1	6941	−6%	−42%
Comb 1	24.6	R	R*	80%	70%	3.7	2.5	3	2359	3	182.1	10,707	+45%	−10%
Comb 2	8.0	R	R*	80%	70%	3.7	2.5	3	2359	3	154.8	9104	+23%	−23%
Comb 3	8.0	R	R*	80%	80%	3.7	2.5	3	2359	3	138.0	8114	+10%	−32%

Note: The letter “E”, shown in variables V2 and V3, is from the Climate File obtained from the ”Standard”. The letter “R”, on the other hand, is from a file with more “Real” temperatures, as detailed in Table 5. “R*” is the same file; only the GD tool is activated, as pointed out in the same table.

, the impact of the variables on the annual heating energy consumption of the Base Case concludes in eight simulated adjustments, of which one was analyzed with a variation. This difference in consumption percentage was measured by simultaneously simulating the Base Case and independently adjusting only one or more variables per case. These results enabled identifying which variables were associated with the performance gap in vulnerable households. Adjustments 1, 5.1, and 5.2 were the ones that generated the most significant increase in energy consumption: the first one associated with the variable V1 “Infiltrations” with an increase of 25% and Adjustments 5.1/5.2 that regulated the variable V5 “Equipment Performance Coefficient” to a COP of 0.7 and 0.8, respectively, producing a 40% and 23% consumption gap over the Base Case value. On the other hand, the cases that made the most significant decreases in heating consumption were Adjustment 4, which modified the heating and cooling limit (V4) at daily temperatures based on the ASHRAE-55 2017 adaptive comfort model with a +3.5 °C to −3.5 °C band of 80% acceptability, reducing consumption by 21%. Finally, in Adjustment 8, variables V9 and V10 were regulated by adopting the activation schedules seen onsite, leading to a 42% reduction in heating consumption directly related to occupant behavior.

After this, the heating energy performance gap was measured. The Base Case represents all the objective parameters proposed by the ECSV, considered in the (theoretical) design stage of a future construction. It yields a consumption of 202.0 kWh/m²-year, which results in a gap of 60% compared to the 125.7 kWh/m²-year of the Real Case, corresponding to the (actual) operation stage. This value is reflected according to the increased and decreased consumption of the studied variables.

Finally, as the value of the Base Case turned out to be higher than the Real Case, it was decided to evaluate how to reduce consumption and close the gap further. First, it was agreed to assess a combination containing the Built Case Comb 1 data since this scenario represents the reality closest to the homes studied. This first simulation meant a little more than a 15% decrease in the gap. Subsequently, work was conducted on decreasing those variables that generated an increase in consumption, such as infiltration (V1) and COP (V5), but not those variables that already decreased it, such as the heating and cooling limit (V4) and the variables (V9 and V10) associated with schedules, since these latter variables were values stated by users and it would be inconsequential to modify them. On the other hand, infiltration is a variable that depends on several factors, which is why its condition could be adjusted for evaluation. This way, combination 2 was developed (Comb 2), maintaining the same conditions as the Built Case (Comb 1). Still, only the infiltration was adjusted, considering a value of 8 ac/h at 50 Pa according to the manual [37] proposed for all the housing in general of the commune of Los Angeles. This value coincides with the value projected in the ECSV, reducing the gap to 23%. Finally, in combination 3 (Comb 3), the variables of Comb 2 were left untouched. Only the heating equipment performance coefficient was adjusted to the value of Adjustment 5.2, which indicates a maximum performance of 80% according to the efficiency for wood-burning heaters given the replacement of appliances through a ministerial program, with Classification B, taken from the document [39], with a difference of 10% remaining between the simulated and real cases.

4. Discussion

The research results showed that certain variables, such as infiltration, equipment performance, and heating schedules and setpoints, significantly impact energy performance in vulnerable households of Central-Southern Chile.

Although several authors agree on which parameters should be considered when calculating a dwelling’s energy consumption, there is no single formula. It is a multifactorial problem and depends on as many dimensions as it poses [31]. However, it became necessary to start from a base, categorizing and identifying which variables impact energy performance according to what is indicated in existing local standards and literature, taking as an example the order proposed by [1], who classifies it into six categories. We can measure it in three stages: design, construction, and operation.

Once the study variables were obtained and each field filled with data extracted from the Standard and the fieldwork, it was possible to measure the impact on the Base Case through simulations. The results indicated that, during the simulation, the variables that increased the heating energy consumption the most were infiltration (V1) and the equipment performance coefficient (V5), by 25% and 40%, respectively. This 25% gap obtained in the infiltration parameter can be contrasted with the 60% stated by [45]. However, there is a similarity in pointing out that it is still a variable that significantly impacts the house’s energy consumption and is still complex to detail in the simulation.

On the contrary, some variables decreased the CEC, such as the heating and cooling limit (V4) and the equipment activation and ventilation schedules (V9 and V10). These parameters may be related to occupant behavior, which appears as one of the most cited reasons in research for the performance gap, as demonstrated by [8] in their review, where 70% of the documents blame the user as the most incident reason for the gap. However, only 40% included formal evidence, and 14% did so with detailed monitoring data. This does not mean we do not have to consider it a cause; instead, we must pay attention to each context and reality studied. On the other hand, [2] estimates that the energy savings contributed to homes by behavior range between 10% and 25%. Instead, [46] found a variation of 33% in heating consumption, close to a 42% decrease in heating energy consumption obtained, which coincides with the number of hours of use shown in Table 6, compared to the Standard corresponding to a 24/7 operation.

Although the variables evaluated impact housing in any context, the variables that have the most significant impact on the variation in consumption may be conditioned by the vulnerability characteristics of the population evaluated. Of the variables that increase heating consumption, the level of infiltrations of a house is directly related to the quality of its construction, and the COP of the equipment will depend on the economic capacity that the inhabitants have to acquire equipment with a better energy category. Of the variables that reduce heating consumption, the range of comfort temperatures that are considered acceptable, as well as the activation schedules of heating and lighting, are directly related to sociocultural practices. However, in contexts of vulnerability, they are influenced by the affordability of energy, where the economic difficulty in accessing it hides energy underconsumption.

Once the impact of the variables on heating energy consumption was analyzed separately, it was interesting to know how the house behaved by evaluating all the study parameters, thus being able to measure the gap. An investigation in Denmark conducted by [47] analyzed the energy consumption of more than 200 practically new single-family homes and compared it with the energy demand proposed by local standards, which showed additional deviations between 25% and 80%. On the other hand, [48] estimated that for the least energy-efficient homes the actual energy consumption for heating is systematically 30% lower than the theoretical one, on average. If we compare these figures with the CEC that was determined for the investigation’s Base Case (202.0 kWh/m²-year) versus the Real Case (125.7 kWh/m²-year), we see an EPG of 60%, a percentage closer to the values proposed by the Danish study. However, the possibility that a “pre-bound effect” is being presented in the energy performance of the investigated homes is not ruled out.

5. Conclusions

This work has evaluated a dwelling under the objective parameters (buildings and use) proposed in the Sustainable Construction Standards for Housing, comparing it with the actual housing conditions reviewed in the fieldwork of vulnerable households in Central-Southern Chile. This made it possible to identify the differences that the different variables generate in predicting heating energy consumption, which is the main contribution made by this research.

It has been possible to adjust the heating energy consumption on the Base Case studied, calibrating the variables associated with activation limits and usage schedules for over 60% of the consumption between both factors. This allowed the energy performance gap between the studied cases to be cut. However, it is worth highlighting that another set of variables with a significant impact on consumption that affected the performance gap was identified. These were climate variations, ground temperature, lighting load and schedule, and equipment performance.

Firstly, thanks to the review of the state of the art and the analysis of local regulatory standards, it was possible to identify the variables that impacted the dwelling’s energy consumption, being able to design and apply a survey to families in the commune of Mulchén, aimed at obtaining relevant data for calculating the impact of these variables. These data allow the values to be orderly and classified as needed to calibrate the simulations. The energy information of the model was even complemented by adding a variable that the ECSV does not consider, such as ground temperature, whose impact generated a difference of over 10% of the energy consumption of the Base Case. In fact, through the Built Case, it was possible to calibrate a simulation according to the reality experienced by the families of the study area, laying the basis for a comparative analysis of the performance gap.

Secondly, upon studying the impact that the variables associated with energy performance had on a Standard proposed by public policies, it was possible to reveal the particularities of vulnerable households in Central-Southern Chile and how some of their parameters are being overestimated. A set of variables that impacted 20% of the consumption stand out. These included infiltration, equipment performance, and heating activation limits and schedules, the latter two associated with the behavior of a particular user.

A result worth highlighting is the measured impact on the heating activation limit variable. Although we can point out that the current standard is following a good path, the evaluation based on the adaptive comfort method with periodic setpoints showed that it can still be improved further. It proposes much more detailed temperature limits and, according to reality, with values per day instead of monthly as the Standard proposes. This change would reduce energy consumption by more than 20%.

Another result worth mentioning is the equipment’s activation schedules, which differ significantly from the energy evaluation criteria used in the ECSV. These schedules outline a permanent heating condition in the homes, which is very difficult to achieve, unlike the use profiles proposed by this research, detailed by month and number of hours, associated with what was seen in the surveys and the literature. Public bodies should review this parameter since these differences can lead to overestimating families’ energy savings.

On the other hand, the analysis of Table 11 made it possible to quantify the energy performance gap generated between the proposed theory of the ECSV and the reality measured in the vulnerable households of the study. This resulted in two proposals for reducing the gap through simulations based on the variables of the Built Case: adjusting first the infiltration to 8 ac/h at 50 Pa, reducing the gap to 23%, and finally, a model based on this combination, modifying the heating equipment performance by 80%, resulting in a performance gap of just 10%.

Finally, it can be concluded that this research has managed to identify those parameters that most impact the CEC of the homes of vulnerable families. This knowledge can contribute to the future design of public energy-efficiency policies that align with social realities through simulations that can consider the proposed values to recreate more reliable energy consumption and cut the energy performance gap, avoiding pre-bound or rebound effects and benefiting future families through homes designed and built based on a renovated Sustainable Housing Construction Standard, improving living conditions and reducing building costs for the state, resulting in a greater scope to solve the housing deficit of vulnerable families in the country.

These findings should be measured cautiously due to methodological limitations that could compromise the external validity. The first limitation is related to the scope of the study. Since it is not a longitudinal study, it is not possible to observe changes in energy use patterns over time, particularly in response to energy price increases, interventions, or changes in socioeconomic conditions. The second limitation is with the research’s non-random design. While a random sample could improve the representation of households, it is challenging to implement because a household’s vulnerability level is unknown beforehand. Therefore, the sample used consisted of households that had participated in the “Con Buena Energía” program (PCBE) of the Ministry of Energy of the Government of Chile, ensuring that these households were within the most vulnerable 40% of the municipality. The third limitation is the case study selected for the simulation, which, although representative of the registered homes, is not generalizable in its results since it needs more cases to investigate. Finally, adjusting the variable associated with equipment performance can require data that are difficult to obtain due to the complexity of measuring that coefficient in housing.

If we want to reduce the performance gap, especially in the homes of vulnerable families, we must improve its accuracy at the design stage, especially in the variables of infiltration, equipment performance, and heating activation schedules and setpoints, which must be closer to local realities. Only in this way can we create more solid simulations with more representative energy consumption, which would contribute to energy policies to reduce social gaps. Ultimately, it is essential to point out that future research may discuss the need to develop different occupation profiles related to consumer realities, use setpoints with adaptive comfort methods, and the relevance of including reliable data on equipment performance, of which there is very little information, in addition to evaluating infiltrations more completely, since they depend on many environmental factors, urban and local context, and are often overestimated.

Similarly, based on this study’s results, it is estimated that future research should consider the methodology used to analyze the country’s different climatic zones and evaluate the values that the Standard has designated for those areas, proposing new adjustments that allow reducing the gap and calibrating the simulations more efficiently.

It is also necessary to complement the knowledge because very few studies focus on the gap of the construction stage, where bad practices on site, improper commissioning of specific construction processes, wear of materials, and lack of follow-up on operations could have a high impact on the projected energy consumption. That is why it is vital to encourage study in these areas and welcome these gaps to contribute to reducing execution errors and related low performance. This is a problem that must be addressed together.

Finally, it is important to highlight that we are in a complex climate scenario where it is urgent to make changes and guarantee the correct design, construction, and operation processes, where several players impact the energy performance of buildings, from those who make political decisions to manufacturers, designers, builders, and housing users, among others, and share a common goal of reducing the gap, which should translate into a more efficient and fair regulatory framework for families.