Data Analysis of the Situation of the Residential Sector in Extremadura and Its Energy Classification

Abstract

This study explores sustainable practices in energy resource management, with the aim of optimizing energy consumption while maintaining quality of life. It focuses on energy efficiency in the residential sector of the autonomous community of Extremadura. The methodology involved collecting data from 50 households, using a computer tool to assess and classify energy efficiency levels. The results showed that homes built before 1994 had low energy ratings, whereas those built afterward demonstrated improvement, reflecting a growing environmental awareness. It was found that small investments, such as the installation of control devices and the adoption of conscious consumption habits, can significantly improve energy efficiency. The study highlights the importance of raising community awareness about energy efficiency and notes that, despite limitations in the data, the findings provide valuable insights for future interventions. In conclusion, energy efficiency is key to a sustainable future, and we all share the responsibility of improving it.

1. Introduction

Energy efficiency [1,2,3,4] is a crucial global priority for addressing climate change [5] and promoting sustainability [6,7]. This concept involves maximizing energy performance while minimizing consumption, thereby reducing operational costs and decreasing greenhouse gas emissions [8] and reliance on non-renewable resources [1,9]. The adoption of sustainable practices, such as the use of renewable energies and efficient technologies, is essential for reducing the carbon footprint and protecting the environment [10].

In Spain, the transition towards greater energy self-sufficiency has been supported by key policies and regulations, such as Royal Decree 235/2013 and Royal Decree 390/2021 [11,12,13,14,15], which govern the certification of energy efficiency in buildings. These measures, aligned with European directives such as Directive 2010/31/EU [12] and Directive 2012/27/EU [16], aimed to establish minimum standards and promote energy renovation. Directive (EU) 2018/844 [13] and the European Green Deal [17] underscore the importance of renovating buildings and adopting smart technologies to achieve climate neutrality by 2050.

One of the most effective strategies in the energy rehabilitation of buildings is the application of external thermal insulation. This measure, which involves cladding façades and roofs with insulating materials, significantly reduces heat losses in winter and helps maintain coolness in summer, thereby decreasing the energy demand for heating and cooling [18]. External insulation not only promotes substantial energy savings but also enhances thermal comfort within homes by reducing temperature fluctuations [19,20,21]. Furthermore, this intervention contributes to the reduction in greenhouse gas emissions, supporting sustainability and climate neutrality goals. In Spain, where a significant portion of the building stock has insulation deficiencies, this strategy can play a crucial role in the transition toward a more efficient and sustainable urban environment [22].

At a regional level, Extremadura has significant potential to utilize renewable resources, particularly solar energy [23]. However, despite the existence of national and European policies, energy efficiency in the residential sector of Extremadura has been the subject of little specific research. This study addresses this gap by analyzing the current state of energy efficiency in residential homes in the region, identifying areas for improvement, and providing practical recommendations.

The main objective is to evaluate how energy consumption in homes in Extremadura can be optimized, contributing to regional sustainability and supporting the transition towards more responsible energy practices. The results of this research have the potential to influence local policies and promote a more sustainable energy future for the region, benefiting both residents and the environment.

2. Materials and Methods

In this study, the most relevant Spanish regulations have been considered as primary references. In particular, the UNE 60364-8 standard [24], which establishes the requirements and recommendations for the electrical part of the energy management system, and UNE ISO 50001 [10,18] have been used as a starting point [11,25].

The UNE 60364-8 standard is related to UNE ISO 50001, which aims to outline the requirements for implementing and improving an energy management system (EMS). This methodology continuously and sustainably enhances an energy system, optimizing consumption across different infrastructures. Furthermore, the effectiveness of an EMS depends on the commitment and availability of all parties involved in the organization [26].

According to UNE ISO 50001, the energy management system is based on four fundamental steps for its implementation:

Step 1, Plan: The energy policy and energy management team are established, and improvements, objectives, and targets are created for later evaluation.

Step 2, Do: The necessary documentation for the system is created, and action plans are put into practice.

Step 3, Check: Progress is analyzed, and follow-up and control proposals are made. Additionally, the keys and objectives of operations that determine energy efficiency are studied, reporting on the results obtained.

Step 4, Act: Achievements are recognized, decisions are made to improve upon previous actions, and a conformity assessment is conducted.

The UNE 60364-8 standard [24] provides and describes the method for obtaining the energy efficiency level of a dwelling through a series of levels ranging from 0 to 5, as shown in

Table 1. Efficiency level of electrical installation efficiency classes.

	Energy Efficiency 0 (EE0)	Energy Efficiency 1 (EE1)	Energy Efficiency 2 (EE2)	Energy Efficiency 3 (EE3)	Energy Efficiency 4 (EE4)	Energy Efficiency 5 (EE5)
POINTS	From 0 to 14	From 15 to 30	From 31 to 49	From 50 to 69	From 70 to 89	90 or older

. Additionally, it covers modifications and renovations made to existing homes to improve their energy efficiency.

This regulation focuses on reducing energy consumption through improvements in electrical installations, such as equipment replacement, inspections, and so on. The mission is to create an efficient design that meets the user’s needs without altering their daily habits.

The six levels of energy efficiency, as shown in Table 1, are obtained through a series of points, outlined in

Table 2. Parameter values.

Parameter	Application Values	Points
II01	<40%	0
	≥40% and <50%	2
	≥50% and <60%	6
	≥60% and <80%	10
	≥80% and <90%	16
	≥90%	20
EM01	<40%	0
	≥40% and <60%	1
	≥60% and <80%	2
	≥80%	3
EM03	<10%	0
	≥10% and <50%	4
	≥50% and <60%	10
	≥80%	16
EM04	0 or less than 80% of circuits are considered	0
	1	2
	2	5
	3	10
	4	15
	More than 4	20
EM05	0	0
	≥1 and <2	4
	≥2 and 3	10
	≥3 and <4	16
	≥4	20
EM08	No consideration	0
	Temperature Control	6
	Room Level Temperature Control	12
	Temperature and Level Control at Room Level	18
EM09	<10%	0
	≥10% and <50%	2
	≥50%	6
BS01	<5%	0
	≥5% and <30%	2
	≥30% and <60%	3
	≥60% and <80%	4
	≥80%	6
BS02	<5%	0
	≥5% and <15%	1
	≥15% and <30%	2
	≥30%	3

, dictated by the standard. These points are defined by a series of parameters.

-

II01: Power consumption.

-

EM01: Zones.

-

EM03: Demand response.

-

EM04: Mallas.

-

EM05: Uses.

-

EM08: Control HVAC.

-

EM09: Lighting control.

-

BS01: Renewable energy.

-

BS02: Electrical energy storage.

Some parameters, such as II01, EM01, and EM03, are determined by the following quotient, and depending on the criterion, the variables a and b are one value or another.

$$k_x={\displaystyle \frac{a \ast 100}{b}}$$

(1)

In parameter II01, the energy consumption of the dwelling is determined; factors a and b represent the annual energy consumption of the measured loads and the consumption of the installation, respectively.

Parameter EM01 addresses the zones within the installation, where a is the surface area of the defined zones and b is the total surface area.

Lastly, parameter EM03 relates to the demand of the devices connected to the installation; a is the assigned power of the receivers with load shedding, and b is the total power of the installation.

Subsequently, parameter EM04 focuses on circuit classification, considering a series of criteria that take into account the circuits responsible for more than 80% of the total energy consumption of the installation, in order to determine its networks.

Parameter EM05 addresses the installation of corresponding power meters and monitoring devices to obtain accurate measurements of energy consumption for specific uses.

Following this, parameters EM08 and EM09 deal with identifying the installed air conditioning capacity and lighting control, respectively. In particular, parameter EM09 represents the relationship between the annual energy consumption for automatically controlled lighting and the total annual energy consumption of the lighting installation, which is determined by various types, percentages, or values as detailed in the following table (see Table 2).

Parameter BS01 indicates the relationship between the annual energy production from renewable sources and the total energy consumption of the installation. Meanwhile, BS02 is defined as the maximum power of the energy storage systems divided by the total energy consumption of the installation and the number of days in a year (365 days).

With the defined parameters, a form was created, available in both digital and paper formats, which includes a series of questions regarding the contracted tariff, consumption, and energy generation. This form can be found at the following address (see

Table 3. Quiz Questions.

Section	Question	Description
1	Email	A means of contact with the customer is needed in case there is a disagreement with the results
2	Locality	Locality where the house is located
	Year of construction	The year the house was built
	Annual consumption	Total consumption of the electrical installation
	Total area	Square meters of the house
	Consumption meters	Meters to monitor your home’s consumption
3	Meter and network	The meter gives a value equal to that of the network
4	Connecting the Meter	What circuit the meter is connected to, the meter’s brand consumption, and the surface area it measures
5	Meter connected to the power outlet	Meter that controls the consumption of the mesh to which it is connected
6	Number of connected meters	The total number of meters connected to outlets
	Surface	Surface area in square meters measured by the above devices
	Consumption	Number measured by meters, annually
7	Contracted power	Power, in kilowatts, contracted on the bill
	Ballast removal devices	Devices to turn off the device to which it is connected when there is a peak in consumption
8	Ballast Shedding Power	Power sum of those devices that are disconnected
9	Conditioning system	Ventilation and/or heating system
10	Air conditioning in all rooms or in some rooms	Ventilation and/or heating system in all rooms or in some areas of the house
11	Climate control with non-time-controlled or time-controlled thermostat	How the HVAC system is controlled
12	Lighting control	Installing a device to control the lighting in the home
13	Controlled and total lighting consumption	Consumption values of the lighting in the house, controlled and total
14	Renewable energy sources	Devices to generate self-sufficient energy
15	Total energy production	Value of energy generated by renewable means
	Storage Devices	Solar batteries are available in the house
16	Battery Capacity	Battery value, in Ah,
	Battery voltage	Battery V-Value

) [27].

The form consists of a series of questions divided by sectors. It begins with the personal details of the client in case contact is necessary. Additionally, the form inquires about their consumption, property size, and location. It continues with a list of devices used in the home to measure and control consumption, along with their recorded values.

Next, it addresses the presence of air conditioning systems, their distribution, and lighting control. The form concludes with questions about the renewable energy sources available in the home for self-consumption and their storage.

Once a sample of 50 households was obtained, the energy efficiency value was calculated using the Excel tool [11].

This tool is classified into two categories: residential and industrial. It begins with the input of client data, such as name, surname, and location of the dwelling. The program allows the user to input these data in a simple and straightforward manner. Details regarding the parameters to be completed and how to perform so within the program are outlined in the following paragraphs.

Once all the data have been entered, the tool generates a report with the energy rating of the dwelling and a series of measures to enhance that efficiency value.

The process begins with parameter II01, energy consumption, the values for which are provided in Sections 2, 3, 4, 5, and 6 of the questionnaire (see Table 3). Sections 2 and 5 clarify the values for annual consumption and the number of meters in the electrical panel and sockets, while Sections 3, 4, and 6 detail the consumption values of the electrical panel and/or sockets depending on whether they are connected to the grid or on an independent circuit.

Parameter EM01, zones, is also covered in the aforementioned sections, as it asks for the total surface area of the dwelling, as well as the area measured by the devices mentioned earlier.

EM03, demand response, is filled out using Sections 7 and 8, where the contracted power and load-shedding power values are requested; this latter value is the sum of the power of those devices that are disconnected during a peak consumption period.

The EM04 factor, the networks, is unlikely to be known precisely at any time since it is a subjective value, that is, it represents the number of criteria that the resident has in their home for, for example, turning the air conditioning system on and off during summer or heating in winter. Therefore, it was decided to always assign the minimum value of 1 criterion per dwelling.

The data obtained from parameter EM05, uses, is related to the measurements of the previously mentioned devices; each measurement is used in a circuit of the house or in a zone, as the sum of all these constitutes the total number of uses. However, as with the previous data, EM04, it is unlikely to precisely know such a value, so it was decided to assign 1 and <2 uses for each dwelling.

Parameter EM08, HVAC control, is addressed through Sections 9, 10, and 11. The first asks whether there is a climate control system in the residence, the second asks if it is installed throughout the house or only in one zone, and the last one checks if there is a thermostat with or without timing control.

Factor EM09, lighting control, is resolved using Sections 12 and 13, which inform about the existence of such control in the dwelling and the total values of consumed and controlled lighting.

BS01 addresses renewable energies, relating to Section 14 and partially Section 15, where it inquires whether such sources are present in the residence and what the consumption value is through them.

Lastly, BS02, electrical energy storage, is found in Section 16, where the capacity and voltage of the battery from renewable energy sources are reported. However, the value obtained is not the one provided by the Excel program; instead, a conversion to kW must be performed as shown in the following equation.

$$Ah \ast V=Wh$$

(2)

In the previous equation, it is indicated that the two values provided in the questionnaire must be multiplied, the value obtained is divided by the hours of operation of the renewable sources that are in the home and finally, it is converted to kW whose value will be the final one.

We finish, with the sum of all the points obtained in the different parameters, obtaining the value of energy efficiency as shown in Table 1.

3. Results

3.1. Analysis of the Results

At this stage, we will examine various approaches using the results obtained from the 50 dwellings regarding their energy efficiency.

To ensure the validity and applicability of these findings, it is essential to delve into the representativeness of the sample, which has been conducted considering the location (Figure 1), the type of dwelling (Figure 2), and the energy consumption (Figure 3) of each home. Although no specific selection process was carried out for the analyzed households, it was taken into account that all the dwellings were located in the Autonomous Community of Extremadura, as the study focused on this region. This methodology allows for a regional approach that, while limited in selection, provides a solid foundation for interpreting energy efficiency patterns in similar contexts.

Figure 1 shows that most of the analyzed dwellings are concentrated in the province of Badajoz, which reflects a significant geographical representativeness in the context of the study.

In Figure 2, the size of the dwellings is analyzed, classifying them into three categories based on the number of occupants: small (one–two people), medium (three–four people), and large (five or more people). The data reveals that the majority of the dwellings correspond to the medium-size category, which is consistent with the predominant demographic structure in the region, where families of four members are common. This is followed by small-sized dwellings, with 17 cases recorded, and finally, large dwellings, with a total of 12. This distribution provides a representative view of the occupancy profile in Extremadura, allowing for the adaptation of energy efficiency strategies to the specific characteristics of each group.

In the analysis of energy consumption (Figure 3) of the 50 dwellings, it is observed that most present moderate consumption, ranging between 2000 kWh and 5000 kWh annually. This range indicates an adequate average energy use for the size and typical occupancy of households in the region. However, some dwellings stand out for their high consumption, reaching up to 8000 kWh, which could be attributed to factors such as the larger size of the dwelling or intensive use of electrical systems, especially in areas with high temperatures. In contrast, there are dwellings with low consumption, likely associated with smaller properties and fewer occupants, suggesting a correlation between the size of the dwelling and total energy consumption.

With all the preceding information, a sensitivity analysis has been conducted to identify the differences in outcomes based on the individual characteristics of each dwelling, the impact of household size, and the energy consumption patterns.

In the sensitivity analysis, a complementary approach is employed to examine the 50 housing cases in terms of energy efficiency. This method allows for the assessment of how outcomes fluctuate when modifying factors such as household size and energy consumption patterns. By analyzing these variables, trends, and patterns that may not be apparent in a standard analysis can be identified, thereby providing a deeper understanding of how various characteristics of the homes affect their energy efficiency. This type of analysis not only enriches the research but also aids in formulating more effective strategies for optimizing energy consumption in the region.

Household size is categorized into three groups: small (one–two people), medium (three–four people), and large (five or more people). Energy consumption patterns are classified as low, medium, or high (based on annual energy consumption prior to the intervention).

A percentage of expected savings is established (see

Table 4. Percentage of consumption reduction.

Group	Energy Consumption Reduction (%)
Small, medium consumption	15%
Small, low consumption	10%
Small, high consumption	20%
Medium, medium consumption	20%
Medium, high consumption	25%
Medium, low consumption	15%
Large, high consumption	30%
Large, medium consumption	25%
Large, medium consumption	20%

) for energy efficiency interventions corresponding to each combination of household size and consumption pattern.

For each group, the reduction is applied to the average energy consumption. It is known that each dwelling initially consumes kWh per year, obtained from various forms detailing the consumption values for each home [28]. We calculated the post-intervention consumption (see Table 5) using Formula (3).

$$Final Consumption=Applied Reduction \left(\%\right) \ast Housing Consumption\left(\mathrm{k}\mathrm{W}\mathrm{h}\right)$$

(3)

To obtain the savings for each dwelling, formula (4) is employed.

$$Savings \left(\mathrm{k}\mathrm{W}\mathrm{h}\right)=Housing Consumption\left(\mathrm{k}\mathrm{W}\mathrm{h}\right)-Final Housing Consumption\left(\mathrm{k}\mathrm{W}\mathrm{h}\right)$$

(4)

Table 5. Savings of the Dwellings.

Dwelling	Household Size	Energy Consumption	Consumption per Dwelling (kWh)	Reduction Applied (%)	Final Consumption per Dwelling (kWh)	Savings (kWh)
1	Large	Medium	3000	25%	750	2250
2	Small	Medium	2400	15%	360	2040
3	Medium	Low	100	10%	10	90
4	Large	Medium	3000	25%	750	2250
5	Medium	Medium	2880	20%	576	2304
6	Medium	Medium	4023	20%	805	3218
7	Medium	Medium	1323	20%	265	1058
8	Medium	Medium	4023	20%	805	3218
9	Medium	Medium	4700	20%	940	3760
10	Medium	Medium	4023	20%	805	3218
11	Medium	Medium	4023	20%	805	3218
12	Medium	Medium	4023	20%	805	3218
13	Small	Medium	1972	15%	296	1676
14	Small	Medium	1077	15%	162	915
15	Small	Medium	2891	15%	434	2457
16	Small	Medium	4400	15%	660	3740
17	Small	Medium	3200	15%	480	2720
18	Small	Medium	3360	15%	504	2856
19	Small	Medium	4937	15%	741	4196
20	Large	Medium	4624	25%	1156	3468
21	Small	Medium	3600	15%	540	3060
22	Small	Medium	2.131	15%	320	1811
23	Medium	Low	300	10%	30	270
24	Medium	Medium	2912	20%	582	2330
25	Medium	Medium	1442	20%	288	1154
26	Large	Medium	1308	25%	327	981
27	Large	High	5540	30%	1662	3878
28	Medium	Medium	257	20%	51	206
29	Medium	Medium	181	20%	36	145
30	Large	Low	1514	20%	303	1211
31	Large	High	7630	30%	2289	5341
32	Small	Low	123	10%	12	111
33	Small	High	5030	20%	1006	4024
34	Large	Medium	2874	25%	719	2156
35	Small	Low	60	10%	6	54
36	Medium	Low	926	10%	93	833
37	Medium	Medium	4985	20%	997	3988
38	Large	High	6000	30%	1800	4200
39	Small	Medium	2900	15%	435	2465
40	Large	Medium	1800	25%	450	1350
41	Medium	Medium	1348	20%	270	1078
42	Large	High	7886	30%	2366	5520
43	Medium	High	5900	25%	1475	4425
44	Medium	Medium	1321	20%	264	1057
45	Medium	Medium	1800	20%	360	1440
46	Large	High	4701	30%	1410	3291
47	Small	Medium	3.688	15%	553	3135
48	Small	Low	300	10%	30	270
49	Small	Medium	2735	15%	410	2325
50	Medium	Medium	3406	20%	681	2725
TOTAL						116,706

Table 5. Savings of the Dwellings.

Dwelling	Household Size	Energy Consumption	Consumption per Dwelling (kWh)	Reduction Applied (%)	Final Consumption per Dwelling (kWh)	Savings (kWh)
1	Large	Medium	3000	25%	750	2250
2	Small	Medium	2400	15%	360	2040
3	Medium	Low	100	10%	10	90
4	Large	Medium	3000	25%	750	2250
5	Medium	Medium	2880	20%	576	2304
6	Medium	Medium	4023	20%	805	3218
7	Medium	Medium	1323	20%	265	1058
8	Medium	Medium	4023	20%	805	3218
9	Medium	Medium	4700	20%	940	3760
10	Medium	Medium	4023	20%	805	3218
11	Medium	Medium	4023	20%	805	3218
12	Medium	Medium	4023	20%	805	3218
13	Small	Medium	1972	15%	296	1676
14	Small	Medium	1077	15%	162	915
15	Small	Medium	2891	15%	434	2457
16	Small	Medium	4400	15%	660	3740
17	Small	Medium	3200	15%	480	2720
18	Small	Medium	3360	15%	504	2856
19	Small	Medium	4937	15%	741	4196
20	Large	Medium	4624	25%	1156	3468
21	Small	Medium	3600	15%	540	3060
22	Small	Medium	2.131	15%	320	1811
23	Medium	Low	300	10%	30	270
24	Medium	Medium	2912	20%	582	2330
25	Medium	Medium	1442	20%	288	1154
26	Large	Medium	1308	25%	327	981
27	Large	High	5540	30%	1662	3878
28	Medium	Medium	257	20%	51	206
29	Medium	Medium	181	20%	36	145
30	Large	Low	1514	20%	303	1211
31	Large	High	7630	30%	2289	5341
32	Small	Low	123	10%	12	111
33	Small	High	5030	20%	1006	4024
34	Large	Medium	2874	25%	719	2156
35	Small	Low	60	10%	6	54
36	Medium	Low	926	10%	93	833
37	Medium	Medium	4985	20%	997	3988
38	Large	High	6000	30%	1800	4200
39	Small	Medium	2900	15%	435	2465
40	Large	Medium	1800	25%	450	1350
41	Medium	Medium	1348	20%	270	1078
42	Large	High	7886	30%	2366	5520
43	Medium	High	5900	25%	1475	4425
44	Medium	Medium	1321	20%	264	1057
45	Medium	Medium	1800	20%	360	1440
46	Large	High	4701	30%	1410	3291
47	Small	Medium	3.688	15%	553	3135
48	Small	Low	300	10%	30	270
49	Small	Medium	2735	15%	410	2325
50	Medium	Medium	3406	20%	681	2725
TOTAL						116,706

The analysis focused on household size and consumption patterns suggests that larger homes offer a higher absolute savings potential, indicating that prioritizing interventions in these households could maximize the benefits of energy efficiency. Additionally, households with initially high energy consumption demonstrate greater sensitivity to interventions, underscoring the importance of tailoring savings measures to the specific consumption level of each dwelling [29].

These findings emphasize that interventions should be adjusted considering both household size and energy consumption to achieve an optimal balance between effectiveness and cost-effectiveness. In this regard, energy efficiency policies could benefit from targeted strategies that prioritize larger households with high consumption levels, maximizing the impact of these measures at the regional level [30].

Despite the limitations of the study, which is focused on specific variables, the results are promising and suggest that adapting interventions to these characteristics can lead to significant energy savings. This not only contributes to energy sustainability but also represents an opportunity to reduce energy costs in households, promoting a more responsible and efficient use of resources.

Secondly, and again using the Excel tool, all the results obtained from the forms [28] have been compiled and analyzed from another perspective, showing the percentage of households that possess each of the different parameters.

We begin with II01, which corresponds to the meters in the electrical panel and sockets. In the previous graph, Figure 4, only 12% have meters, and not all of them possess this device; that is, they have either one or two. The remaining 88% do not have this device.

Figure 5 is related to Figure 4, where if they have meters, they have defined areas; therefore, the surfaces where those meters read the consumption are placed. Since most do not have them, the maximum value is 88%, and the rest are those who have a meter.

Figure 6 shows devices that disconnect when it has a very high consumption peak. A total of 84% do not have it and the rest have one or deactivate different equipment.

Figure 7 takes the criteria of the house into account, as it is not possible to know these data. A criterion has been placed in all cases, except in one that has two criteria, hence, 49 of the houses have 98% with one criterion.

Figure 8 shows the uses, as most do not have meters was not possible to determine exactly in all the homes how many uses they have, so it was predetermined that they have at least one use. In total, 96% and two of those homes have more than one use, as determined by the different data obtained, and therefore, they are 4%.

Figure 9 shows if they have air conditioning devices in your home. All houses know this data and if they are in all rooms with thermostat control and with time; therefore, 60% of them have air conditioning equipment with a thermostat in some of the rooms but without time control, and 40% have it at room level, with a thermostat and time control.

In Figure 10, most cases do not have a controlled installation of the lighting in their home (92%). The cases that have it are divided into those that consume a lot and control little (6%) and those that have it well balanced and save (2%).

Most of the cases do not have any renewable energy source (82%), as observed in Figure 11, the rest are divided into two categories: those who have sources and do not save a large amount (14%) and those who save a considerable amount (4%).

None of the homes in the studio have batteries to store the energy they obtain from renewable energy sources.

The analysis also studied the levels of energy efficiency, that is, with the data previously collected, Figure 12 exposes the total energy efficiency values of all cases.

In Figure 12, there are 22 residences with the lowest efficiency value (EE0), the next 18 with EE1, there are only 8 homes with a positive value (EE2), and the remaining 2 properties have an intermediate value of EE3. The other higher efficiency levels have not been achieved in the study.

The conclusion is that there is a very high number of homes that are not efficient; 44% with EE0 efficiency and 36% with EE1.

To conclude the analysis, the year of construction of the houses will be studied to obtain a result on whether over the years there has been evolution and awareness in the implementation of renewable energies.

Looking at the years of construction of the 50 residences (see Figure 13) we conclude that the starting year for dividing the study is 1994 because there was a turning point from that date.

As shown in Figure 13, the dark blue area is the houses built before 1994, in total a number of 17 residences, where only the first two levels of efficiency were taken into consideration, 24% of the houses, a total of 12, did not have any type of device to improve their efficient state, and 10% of the houses, 5 residences, have a value of EE1, that is, their status has improved, but not very high.

The lighter blue areas shown in Figure 13 are related to the houses built after 1994, a total of 33, where there is a greater variety of efficient levels. A total of 20%, ten homes, continue to remain at a low level of efficiency, the same happens with the next level, EE1, where 28% maintain this value. Although it is highlighted that from this year residents are more aware of how important it is to save on consumption and therefore, the values of EE2 and EE3 appear, albeit to a very low extent, 16% and 2%, respectively.

Since the 1990s, we have been more aware that we have to be more reserved with what we consume, to save the planet. It is shown in the graph that before this time there were only two types of efficiency, the lowest EE0 and EE1. On the other hand, after that, we have more variety of efficiency taking into account the higher levels such as EE2 and EE3. In 29 years (1994–2023) we have doubled the number of efficiencies, before this time there were only 2 and now there are 4.

3.2. Measures to Improve Energy Efficiency in Homes

A thorough examination of the possible measures that can be implemented in nursing homes has been carried out and we concluded that the following measures are the most appropriate for different types of housing (see

Table 6. Measurement.

	Measurement
Scenario from 0 to 1	Only meters are placed.
	Meters with ballast removal devices.
	Meters with conditioning system in some rooms.
	Meters with conditioning system in all rooms.
	Conditioning system in all rooms.
	Meters with lighting control.
Scenario 1 to 2	Meters, ballast removal devices, lighting control.
	Gauges, ballast removal devices, conditioning system in some rooms.
	Measuring meters, ballasting devices, conditioning system in all rooms with time control
Scenario 2 to 3	Meters, ballast removal devices, air conditioning system in some rooms, lighting control.
	Meters, ballast removal devices, air conditioning system in some rooms, renewable energy sources.
	Meters, ballast removal devices, air conditioning system in all rooms, lighting control.
	Meters, ballast removal devices, air conditioning system in all rooms, renewable energy sources.
	Meters, ballast removal devices, conditioning system in all rooms with time control.
Scenario 3 to 4	Meters, ballast removal devices, 4 criteria, equal > measured zones 2 and 3, air conditioning system in some rooms, lighting control, renewable energy sources.
	Meters, ballast removal devices, 4 criteria, equal measured zones >2 and 3, air conditioning system in all rooms, lighting control, renewable energy sources.
	Meters, ballast removal devices, 4 criteria, zones measured > the same 2 and 3, conditioning system in all rooms with time control.
	Meters, ballast removal devices, 4 criteria, areas measured > equal 2 and 3, air conditioning system in some rooms, renewable energy sources, batteries > equal 30%.
Scenario 5	Meters, ballast removal devices, 5 criteria, measured areas > equal to 4, air conditioning system in some rooms, lighting control, renewable energy sources, batteries > equal 30%.
	Meters, ballast removal devices, 5 criteria, measured areas > equal to 4, conditioning system in all rooms, lighting control.

).

The aforementioned measures are applicable in any residence seeking to improve its energy efficiency, starting with an assessment of the initial efficiency value of the dwelling. The installation of exterior thermal insulation has been proposed, which, although not listed in the measures table, is recognized for its effectiveness in reducing heat loss, enhancing thermal comfort, and decreasing greenhouse gas emissions. Moreover, this solution is adaptable to different types of homes and climates, being flexible and scalable for energy rehabilitation projects.

It is crucial to consider the architectural diversity of the region. Therefore, interior insulation has been evaluated as a viable alternative, especially in buildings where modifying the exterior could compromise esthetic integrity. The literature supports the effectiveness of interior insulation, which can offer comparable benefits to exterior insulation in terms of energy savings and thermal comfort. To ensure recommendations are tailored to each home, a thorough diagnosis is essential, taking into account the design, materials, and environment of each dwelling [31].

3.3. Validation and Contrast Process

In addition to the 50 residences that were obtained in the study, 5 were reserved to validate and contrast the levels of efficiency, as well as the year of construction and the different parameters. Such information is shown below in

Table 7. Parameters of the remaining 5 dwellings.

Houses	Efficiency Levels	Year of Construction	Points
51	EE1	2003	18
52	EE0	1950	6
53	EE1	1980	18
54	EE0	1932	0
55	EE0	1980	6

.

Table 7 displays the various data from the five residences to validate the study. We begin with the efficiency levels, which, as observed, do not show any higher levels; that is, they remain at the lowest levels, corroborating the conclusion reached in Figure 13.

Next, we look at the year of construction, where four out of the five dwellings were built before the study year, 1994. It was argued that before this date, there was little concern for being more sustainable, which is why, in these cases, the levels remain at the lowest, EE0 and EE1, as shown in Figure 13. The remaining dwelling, with an EE1, indicates that the levels have improved slightly, as demonstrated in Figure 13.

3.4. Cost of Energy-Saving Measures

Various actions have been suggested to enhance the efficiency of the dwelling under analysis. However, what is of greatest interest to the client looking to optimize their home’s energy consumption is the analysis of the costs associated with the different measures to be implemented. Consequently,

Table 8. Cost of energy-saving measures.

	Types	Price (€)	Price (€/h)
DEVICE	Meters	20–26
	Ballast Removal Device	21
	Air conditioning	389
	Thermostat	55
	Radiator	192
	Boiler	1559
	Solar panels (100 W)	100
	Solar panels (175 W)	240
	Solar panels (2* 100 W)	190
	Solar panels with battery (1 kWh/day)	620
	Solar panels with battery (2 kWh/day)	790
	Solar panels with battery (3 kWh/day)	1170
LABOR	Electrician	30 €/h.
	Home automation installer	25,000–45,000 €/year	45
	Solar panel installer (mid-range)	30,000 €/year	38
FACILITIES	Simple air conditioning installation	375
	Multi Split air conditioning installation	550 + (389 * pcs. air conditioner)
	Heating installation (IRI + RITE)	560 + 190
	Home automation installation Apartment (50–100) m2	650–2000
	Home automation installation Single-family house (200–300) m2	2400–4000
	Installation of home automation villa (400–500) m2	5000–8000

* IRI: Individual receiving installation. It is what you need to ensure that the conditions of the gas installation in your home or premises are adequate, and thus, be able to register it. * RITE: The Regulation of Thermal Installations in Buildings is the Spanish regulation that regulates the design, installation and maintenance of air conditioning and domestic hot water production installations.

has been created, detailing the prices of the various devices to be installed, as well as the expenses related to the installations and the labor required to carry out all these improvements.

Table 8 displays the various prices analyzed, which correspond to average prices in Spain. Most of them have fixed costs; however, in the case of the meters, two prices are shown due to the existence of two different types: those that connect to the electrical panel and those that register from the sockets. Additionally, two types of packages for solar panels are distinguished, with or without batteries, and within these variants, different panels can be installed depending on the watts required.

Regarding labor, hourly rates in euros have been considered, as some installations can be completed in less than a day.

Finally, for installations, options such as simple or multi-split air conditioning have been taken into account, depending on the client’s preferences, noting that the price of these options is average. Heating installations have also been considered. Furthermore, there is an option to install home automation for three types of dwellings based on their size.

Next,

Table 9. Costs of the proposed measures.

Scenarios	Maximum Points	Measurement	Points Earned	Types	Costs (€)
Initial 0	0–14	No	0	-	0
From 0 to 1	15–30	1	16	-	56
		2	16	-	77
		3	16	Air conditioning 1	820
				Heating 2 radiators	2749
		4	16	Air conditioning 5	2551
				Heating 7 radiators	3709
		5	18	Air conditioning 5	2495
				Heating 7 radiators	3653
		6	16	-	2201
From 1 to 2	31–49	1	42	-	222
		2	46	Air conditioning 1	841
				Heating 2 radiators	2770
		3	48	Air conditioning 5	2627
				Heating 7 radiators	3730
From 2 to 3	50–69	1	52	Air conditioning 1	2986
				Heating 2 radiators	4915
		2	52	Air conditioning 1	1449
				Heating 2 radiators	3378
		3	52	Air conditioning 5	4717
				Heating 7 radiators	5875
		4	50	Air conditioning 5	2990
				Heating 7 radiators	4148
		5	58	Air conditioning 5	2627
				Heating 7 radiators	3785
From 3 to 4	70–89	1	70	Air conditioning 1	3404
				Heating 2 radiators	5333
		2	70	Air conditioning 5	5135
				Heating 7 radiators	6293
		3	73	Air conditioning 5	2627
				Heating 7 radiators	3785
		4	70	Air conditioning 1	1669
				Heating 2 radiators	3598
4 to 5	90	1	90	Air conditioning 1	3814
				Heating 2 radiators	5743
		2	94	Air conditioning 5	4717
				Heating 7 radiators	5875

The colors in this table reflect the economic level of each option. Green indicates the most economical option, yellow marks the intermediate point between the cheapest and the most expensive, and red corresponds to the highest-priced option.

details all the measurements proposed in Table 6, along with the associated costs obtained from Table 8.

Table 9 presents the different scenarios, taking as a reference a standard dwelling with three bedrooms, two bathrooms, a living room, a kitchen, and an entrance, along with the maximum points that can be achieved by transitioning from one scenario to another. The prices corresponding to each measure have been calculated using Table 8, considering that some measures include a climate control system, which may consist of one or several terminal units, as well as heating with two or five radiators, depending on the client’s preferences. This is because the study addresses the climate control system, taking into account its presence in some or all rooms of the residence.

Initially, the described dwelling has an energy consumption of 3000 kWh, a surface area of 150 square meters, a contracted power of 4.4 kW, and a lighting consumption of 3285 kWh. Based on these data, various scenarios have been generated with the corresponding measures, thereby improving the energy efficiency level in each case and calculating the associated costs for each measure.

3.5. Improvements to Housing with the Proposed Measures

At this point, five residences were selected with initial data, and the measures proposed in Table 6 were implemented to assess whether it is possible to increase the level of efficiency in different cases.

The chosen dwellings, as shown in

Table 10. Improvements in housing with the proposed measures.

	Initial Scenario	Added Measures	Costs (€)	Initial Scenario
CASA 21 (EE0)	Non-meters	Placing Meters	56	House 21 (EE1)
	No ballasting devices
	No renewable energy sources
	Air conditioning in some areas
	Lighting control
HOUSE 52 (EE0)	Non-meters	Install gauges and ballast removal devices	77	House 52 (EE1)
	No ballasting devices
	No renewable energy sources
	Air conditioning in some areas
	No lighting control
HOUSE 34 (EE1)	Non-meters	Placing Meters	56	House 34 (EE2)
	No ballasting devices
	No renewable energy sources
	Air conditioning in all rooms with T and T
	No lighting control
HOUSE 41 (EE2)	If meters	Shedding and lighting control devices	Air conditioning 2196 Heating 2388	House 41 (EE3)
	No ballasting devices
	No renewable energy sources
	Air conditioning in some areas
	No lighting control
HOUSE 7 (EE3)	If meters	More meters are placed The client is proposed to increase criteria to 3	56	House 7 (EE4)
	If ballasting devices
	If renewable energy sources without batteries
	Air conditioning in all rooms with T and T
	Yes lighting control

, include number 7 with an efficiency rating of EE3, numbers 21 and 52 with EE0, the lowest efficiency level, number 41 with EE2, and finally number 34 with an EE1. Efforts have been made to select various levels and types of residences to test the feasibility of the study. Additionally, a list of prices were proposed to observe the costs associated with achieving greater efficiency.

In house 21, with an EE0 rating, it is assumed that the client has no devices to help improve energy efficiency, except for a climate control system in some areas of the house and partial control of the lighting. Therefore, it was proposed to increase its efficiency level to EE1 with a single measure, the most economical one, as shown in Table 10, which is to install meters in the electrical panel and the sockets. Just by implementing this small measure, the house can already move up to the next level.

If the client wanted to implement the other proposed measures to achieve an EE1 rating, they could do so, but it would not be necessary because all the measures increase the number of points to 16, according to Table 10. Therefore, one measure is equivalent to another, and it has been assumed that the client wishes to spend as little as possible.

In house 52, initially rated EE0, the difference from the previous one is that there is no control over the lighting. Therefore, the measure to be implemented would be to install meters in the electrical panel and the sockets, as well as load-shedding devices on some appliances in the house so that they disconnect during peak consumption. The cost of this measure is higher than the previous one, as shown in Table 10.

Additionally, the same situation applies as with the previous case; the points for the three proposed measures are very similar. In other words, it makes no difference whether one measure or another is implemented, as all three would raise the rating to EE1. Therefore, the most economical measure has been presented.

In house 34, with an EE1 rating, has air conditioning in all rooms equipped with a thermostat and time and temperature control. This is an exceptional case, as it already has a significantly higher efficiency level than other cases due to a single measure being implemented. Nonetheless, efforts will still be made to increase efficiency by installing meters in the electrical panel and outlets. This measure is not listed under the section for moving from EE1 to EE2, but as mentioned earlier, it is an exceptional case.

To conclude, with just one economical measure, such as installing meters in the home, it can already elevate the efficiency level to EE2.

In house 41, with an EE2 rating, there are initially meters and an air conditioning system in some areas. Therefore, the proposal is to install load shedding devices on some of the appliances in the residence and to control the lighting at 65%. This case is also exceptional because the measure to be implemented will be partial, as the client already has some devices installed. Consequently, the cost is lower than in Table 8, as the prices for the devices that are already in place have been excluded. The final cost is presented in Table 10, depending on the air conditioning system installed.

With just this measure, which is relatively inexpensive, the efficiency level would reach EE3. Similarly to the previous cases, all the proposed measures yield the same points, so the most economical option has been chosen.

Finally, residence number seven was analyzed, which has an initial energy efficiency level of EE3. This residence has already installed meters, load-shedding devices, renewable energy sources without batteries, and an air conditioning system in all rooms equipped with a thermostat and temperature and time control.

The measure to be implemented is to add three more meters between the electrical panel and the outlets, and for the client to increase their criteria in the home to three. However, this last adjustment incurs no cost, so the price is minimal, as shown in Table 10, in order to achieve the efficiency level of EE4.

4. Discussion

In conclusion, energy efficiency is fundamental in the 21st century, and we all share the responsibility of caring for the planet to ensure its future habitability. This study demonstrates that, in the residential sector of Extremadura, energy efficiency can be significantly improved through practical measures and small investments, such as the installation of consumption meters and load-shedding devices in cooking equipment. Furthermore, the installation of exterior thermal insulation was proposed as an effective enhancement, which, although not detailed in the measures table, was deemed essential in advising the client. This intervention not only reduces heat loss and improves thermal comfort but also contributes to sustainability, especially in a region with high summer temperatures.

The findings indicate that homes built before 1994 have lower energy efficiency, highlighting the need to focus strategies on these older buildings. Modest investments in energy control and responsible consumption habits can lead to significant efficiency improvements, as evidenced by the five homes analyzed, which achieved the highest level of efficiency with minimal investments.

Despite the study’s limitations, it is suggested that local policies promote incentives to improve energy efficiency in older homes and educate residents on efficient consumption. Future studies should explore the impact of various technologies in this field. Enhancing energy efficiency is not only vital for reducing emissions and consumption but can also drive regional sustainability and improve quality of life. The results provide a solid foundation for future interventions that support a more sustainable future in Extremadura.