An Analysis of Energy Efficiency Actions and Photovoltaic Energy in Public Buildings in a Semi-Arid Region: The Requirements for Positive Energy and Net-Zero Energy Buildings in Brazil

Abstract

The search for sustainable energy alternatives is urgent in the face of climate change and resource scarcity. In this context, increasing energy efficiency in buildings through distributed energy resources (DERs) is crucial for sustainability and self-sufficiency. This article aims to analyze the impact of several energy efficiency actions, in addition to the installation of a photovoltaic solar energy system in a public building in a semi-arid region, determining the necessary aspects for such buildings to become positive energy buildings (PEBs) and/or net zero energy buildings (NZEBs). As a basis for the methodology, a case study was carried out in a university restaurant in a semi-arid region in Brazil. Several pieces of data were collected, such as the air temperature, solar radiation, active energy and the number of users in the building. The relevance of each variable in relation to electricity consumption was identified through statistical correlation analysis, resulting in an energy consumption per square meter per year of 80.78 kWh/m²/year and an active energy consumption per user per year of 0.88 kWh/m²/year. Three energy efficiency actions were evaluated and compared technically and economically against the investment in a grid-connected photovoltaic system (GCPVS) for the same building, simulating before and after the entry into force of Law 14.300/2022, which regulates distributed generation in Brazil. The installation of thermal insulation on the building’s roof showed good technical, economic and environmental performance, compared to GCPVS, and proved to be attractive and competitive among the other scenarios. All simulated actions resulted in an annual emission reduction of 14.8 tCO₂e. When calculating the building’s generation potential, it was found that it could be considered an NZEB and PEB.

1. Introduction

In 2023, the energy demand in buildings accounted for 28% of global final energy consumption, but this demand decreased by 0.5% compared to the previous year, likely due to warmer winters in countries with high building-heating needs. In warm regions, including India, the Middle East and Southeast Asia, exceptionally warm weather has also led to much higher electricity consumption for cooling in buildings. Under a net-zero energy scenario, progress in building energy efficiency is expected to increase substantially by 2030, with all new buildings and a significant proportion of existing buildings becoming carbon neutral, which would reduce total final energy consumption in buildings by around 17% [1].

In Brazil, electricity consumption in 2023 was 531,873 GWh, 4.22% higher than in 2022. The North and Northeast regions led the growth, with increases of 7.8% and 7.7%, respectively. The Southeast region concentrates the largest share of the country’s consumption, accounting for 48% of the total in 2023. The industrial sector continues to be the largest consumer, with 35.4%, followed by the residential sector, with 31% [2].

Global, average air conditioning efficiency has increased in recent years, but indirect CO₂ emissions from space cooling are rising rapidly, nearly tripling from 1990 to just over 1 Gton of CO₂ in 2022. Emissions were more than 2% higher in 2022 than in 2021. As the planet warms, ensuring that cooling needs are met equitably and sustainably is critical. Without a shift towards better available products and improvements in the performance of the buildings in which they operate, electricity demand for space cooling in buildings could increase by up to 40% globally by 2030 [3].

The use of air conditioning in the Brazilian residential sector grew 9% per year between 2005 and 2017, totaling 18.7 TWh, and with a projected growth of 48.5 TWh by 2035. The establishment of more rigorous minimum energy efficiency indexes could contribute to reducing electricity consumption in air conditioning to 36.8 TWh in 2035. In other words, in this scenario, avoided consumption could reach 14.5 TWh in 2035, the equivalent of a 3475 MW power plant [4].

The current scenario of energy efficiency in buildings is directly correlated to zero energy buildings (ZEBs), which can be understood as energy independent buildings, but according to [5], a net zero energy building, NZEB, can be defined as a building that produces as much energy as it consumes from on-site renewable energy sources, with energy exchanges with a power grid if the annual net balance is zero. The authors argue that achieving this milestone can be achieved through the installation of on-site power generation and energy efficiency actions, which can achieve up to a 43% reduction in energy use with measures such as high-performance envelopes, installation of equipment and systems, lighting, the use of passive cooling and heating techniques, an action also advocated by the authors in [5].

In recent years, the dissemination of the NZEB concept has been seen as one of the solutions for decarbonization and reduction in electricity demand [6]. Public policies have been established and are being implemented worldwide. According to [7,8], the US government wants to achieve NZEB commercial building construction by 2050; in California, since 2020, new residential constructions need to meet the zero-energy standard; in Massachusetts, the goal is that from 2030, all new constructions will be NZEBs, while the European Commission has a decarbonization target of 80% in the same period.

The authors in [9] classify NZEBs into four distinct categories from an economic and sustainability perspective:

• Net-Zero Site Energy: producing as much renewable energy as it consumes in a year on site.
• Net-Zero Source Energy: an NZEB source produces as much renewable energy as it consumes in a year, accounted for at the source (primary energy used to generate and deliver energy on site).
• Net-Zero Energy Costs: a balance between the amount that would be paid to the energy provider for use and the value of renewable energy injected into the grid through local production used throughout the year.
• Net-Zero Emissions: the production of renewable energy, without pollutant emissions, equal to or greater than the sources that emit pollutants.

In addition to the above classifications, reference [9] presents the definition of near zero-energy buildings, which consists of a building designed to meet one or more NZEB definitions; however, it may not achieve a net zero energy position in operations every year.

Programs for the dissemination of energy efficiency in systems and equipment in Brazil date back to 1984, and to encourage the conservation and efficient use of natural resources in Brazilian buildings, minimizing waste and impacts on the environment, PBE Edifica was created [10]. The Procel Edifica, together with the Brazilian Center for Energy Efficiency in Buildings (CB3Es), launched a new proposal for a method to assess the building’s energy performance, based on primary energy consumption, published in 2023 through Inmetro’s Normative Instructions (INI-C and INI-R). Energy-efficient buildings that have renewable energy generation systems installed locally can be classified as net zero energy buildings or positive energy buildings. The INI-C defines an NZEB as one that is “[…] energy efficient whose renewable energy generation produced within the boundaries of the building or the lot on which the building is located supplies 50% or more of its annual energy demand”, and an EEP as a building “[…] whose renewable energy generation produced within the boundaries of the building or the lot on which the building is located is greater than its annual energy demand” [11].

In this context, a case study for this research was carried out in the restaurant building of the Federal Rural University of Semi-arid Region. The Brazilian northeast has a semi-arid climate region, characterized by high average temperatures and thermal sensations, reaching 38 °C [12]. Due to such temperatures, air conditioning is essential, resulting in high energy demand for this end use. In this sense, this article proposes techniques and technologies to increase the efficiency in the use of electrical energy and the installation of a photovoltaic solar energy system in a public building, evaluating the necessary aspects for such buildings to become positive energy buildings (PEBs) and/or net zero energy buildings (NZEBs).

This article is structured in five sections, the first being this introduction. Section 2 presents a bibliographic review, justifying and contextualizing the topic studied and serving as a basis for the development of the contributions of this work. Section 3 details the methodology and explains how the proposals, databases and calculation methods were developed. Then, in Section 4, the results are presented and discussed. Finally, Section 5 presents the conclusions of this study.

2. Literature Review

According to [13], the energy efficiency of buildings decreases over time due to changes made to buildings to meet new purposes, as well as changes in user behavior. According to the authors, whose research was conducted in Seoul, Korea, the energy efficiency of a building should not be assessed solely based on energy consumption, as seasonality is a fundamental variable in these studies.

The influence of heat input because of increased natural lighting, and the resulting impact on air conditioning and electricity consumption was studied in India, in a city with a semi-arid climate. The case study carried out in a hospital showed that replacing a single-glazed window with a double-glazed window increased the amount of incident light and reduced the effects of heat, reducing the temperature in the environment [14].

The authors in [15] studied the optimization of energy use in hospitals, specifically in Morocco. According to the article, the trend in new generations of sustainable hospitals is to use an intelligent management system for the real-time monitoring of the building’s energy performance with the integration of renewable energies, energy storage and energy recovery.

In the studies in reference [16], also in a semi-arid climate, scenarios of energy efficiency actions in residential buildings in Iraq were simulated. The reduction observed was 49% and roof insulation was the most interesting action from an energy point of view; however, it was not economically viable. The adoption of films and sealing of air outlets provided the best economic return.

Still in the residential context, in a semi-arid climate, the authors of [17] studied the energy efficiency resulting from the use of thermal insulation in Algeria. In the best-case scenario, the reduction in the use of natural gas for heating was 57% in the high-energy-performance building, while the reduction in electricity consumption was 51% in the year.

The study carried out by [18] analyzed bank buildings in the southern region of Brazil to attest to the quality of the INI-C methodology. The study highlighted greater accuracy in calculations on the real building and strengths such as conditions and reference values for different types of buildings other than residential ones and the calculation of energy consumption in kWh/m², facilitating the quantification of energy performance for comparison purposes with peers.

In [19], the Italian nZEB standards were used to investigate the energy and economic benefit of some PV layouts supplying an office building; the PV layouts studied covered an amount of the building’s energy demand ranging from 65% to 158%. The numerical model of the building and its renewable energy plant were simulated with a software package based on EnergyPlus 25.1.0, while the economic evaluation considered two fiscal mechanisms supported by the Italian Government.

A study shows the potential energy-saving alternatives for an eighteen-office building in the central business district of South Jakarta in reference [20]. Four actions were considered, cooling tower and CWP pump replacement, BAS installation, dim LED light replacement, and solar panel installation. This study shows that NZEB is a viable way of improving building efficiency and reduce emissions. Future research can be completed in several directions. To make the results of this study executable, future studies can be focused on NZEB project management. The project should be planned appropriately to minimize disruption to tenants’ daily routine activities to avoid delays and prolong the project.

Green buildings are those that consider the perspective of a sustainable building, that is, that consider energy efficiency, bioclimatic architecture, rational use of resources and other factors. Currently, there is an increase in the protagonism of consumers through buildings, through automation and control devices, and smart buildings will actively integrate the electricity distribution system. Smart buildings, integrated and focused on sustainability in their set of actions, result in the better management of the building’s resources, whether energy, water or human. According to [21], the control of heating, ventilation and air conditioning systems, lighting, energy and environmental control are the main points that determine operational efficiency. With the use of sensors, for example, it is possible to control these types of systems, avoiding energy waste due to forgetting to turn off appliances when they are not in use or due to excessive sizing of a demand that should be lower, resulting in the use of energy beyond what is necessary.

The correlation between green and smart buildings was addressed by [22]. Figure 1 shows the main points that characterize green and smart buildings, as well as the common points that unite the two concepts.

Articles [22,23] have expanded the correlation between building types, including NZEBs. In [23], the authors classify the evolution of buildings into five generations, in which they correlate sustainability and energy consumption characteristics, as follows:

• The 1st generation, called green building, characterized by the beginning of leadership in energy and environmental design (LEED) certification; reduction in energy consumption; concern about the impact of buildings on the environment.
• The 2nd generation, called nearly zero energy buildings (nZEBs), characterized by reduction in energy consumption; support through (limited) policies; concern about the impact of buildings on the environment.
• The 3rd generation, called net zero energy buildings (NZEBs), characterized by reduction in energy consumption; support through (limited) policies; concern about the impact of buildings on the environment.
• The 4th generation, called new generation NZEBs, characterized by consideration about charging stations for electric vehicles; support through consistent policies; economic viability; considerations concerning climate change; local energy storage; smart technology; energy generation (including renewable energy); reduction in energy consumption and concern about the impact of buildings on the environment.
• The 5th generation, future generation, characterized by regenerative effects, creation of a shared economy; distributions of net zero energy; consideration about charging stations for electric vehicles; support through consistent policies; economic viability; considerations concerning climate change; local energy storage; smart technology; energy generation (including renewable energy); reduction in energy consumption and concern about the impact of buildings on the environment.

As a result of discussions on the Kyoto Protocol for the reduction in greenhouse gas (GHG) emissions, the negotiation of emission quotas between the developed countries signatories to the agreement was established, allowing international transfers of GHG emission quotas between countries [24]. In Brazil, there is a bill approved by the National Congress to create the Brazilian Greenhouse Gas Emissions Trading System (SBCE). This regulation will govern the regulated market, as well as establish guidelines for the market. Despite the lack of regulation, Brazil has increased its participation in the generation of emissions offsets in the voluntary market. According to [25], the number of offsets issued in 2018 was 2.2 million credits, while in 2021 it was 45.2 million. Considering that one credit is equivalent to 1 tCO₂e (one ton of carbon dioxide equivalent), Brazil reduced or removed 45.2 million tCO₂e from the atmosphere in the voluntary market in 2021.

3. Methodology

The methodology adopted for this research was a case study using a real building, involving data collection, simulations and analysis. The University Restaurant (UR), Figure 2, is in the city of Caraúbas, the state of Rio Grande do Norte, northeastern Brazil (geographic coordinates: −5.773971772025655, −37.56891706630019). The building under study has a constructed area of 1056 m², consisting of thirty-nine rooms. Its façade is formed by single-glazed windows and four glass panels with external metal louvers at a 45° angle, in the windows with direct sunlight. The air conditioning of the building’s rooms is provided by split-type air conditioning, with and without compressor speed control. The cafeteria is provided by split-type cassette air conditioning.

To carry out the case study, a methodology divided into four main steps was adopted:

• Modeling and Simulation;
• Energy Performance Indicators;
• Statistical analysis;
• NZEB and PEB classifications.

These steps are detailed in Section 3.1, Section 3.2, Section 3.3 and Section 3.4 below.

3.1. Modeling and Simulation

RETscreen is software created by the Canadian government that assists in technical and financial feasibility studies of various types of projects based on benchmarking, such as energy efficiency actions, renewable energy projects, central, distributed, or hybrid electricity generation, heating and cooling projects and many others [26]. It can be used to evaluate the life cycle cost, production or energy efficiency (from different fuels) and reductions in greenhouse gas (GHG) emissions, also relying on a NASA climate database.

The scenarios proposed in

Table 1. Simulated scenarios.

Scenario	Energy Efficiency Action (EEA)
1	Replacing the standard energy savings air conditioning (current) with a variable-speed (premium energy savings) air conditioning
2	Roof covering with 35 mm aluminized thermal blanket.
3	Replacing single glazing with insulated (double) glazing
4	All concomitant actions

, with techniques and technologies to increase energy efficiency in the case study simulated with RETscreen 4.0, were chosen due to the high average temperatures and thermal sensations, in which air conditioning is essential, resulting in high energy demand for this end use. Furthermore, references [14,15,16,17] served as a basis for choosing the scenarios, as they are case studies in semi-arid climate regions in several countries such as India, Morocco, Iraq and Algeria.

In addition to EEA, the tool was also used to study the technical and economic feasibility of a distributed generation (DG) of photovoltaic type connected to the electrical grid. A photovoltaic system was dimensioned, as shown in

Table 2. Components of the photovoltaic system.

Component	Quantity	Power	Description
JAM78S10-440MR Module	111	440 Wp	Efficiency 20.3%, coef. temperature 0.4%/°C, miscellaneous losses 1%. Manufacturer: JA Solar, Beijing, China
GW50KLV-MT Inverter	1	50 kW	Efficiency 98.7%, three-phase, pot. miscellaneous losses 1%. Manufacturer: Goodwe, Suzhou City, Jiangsu Province, China

, using the building’s electricity consumption and solar radiation data in the region.

3.1.1. Data Collection

Several pieces of data were collected for use in the methodology steps.

Table 3. Quantitative variables used.

Quantitative Variables	Observations
Solar global radiation, W/m2	Collected by the Automatic Meteorological Station (AMS)
Air temperature, °C	Collected by the Automatic Meteorological Station (AMS)
Electrical energy, kWh	Exclusive electricity meter for the UR building
Users (number of meals served in the UR)	Computerized system for registering users of the UR building
Average tariff, USD/MWh	Collected by the Brazilian Electricity Regulatory Agency [27]

presents the quantitative variables considered in the modeling and simulation, energy performance indicators calculation, statistical analysis and NZEB and PEB classifications.

The meteorological data presented were collected by the Automatic Meteorological Station (AMS) located inside the university, a few meters from the UR (geographic coordinates: 5.773038922676941, −37.57001938629346). The AMS uses a HOBO RX3000, Manufacturer: Onset, Bourne, MA, USA—Ethernet Station [28] as a measuring device. The station can collect real-time meteorological data every five minutes, such as solar radiation, air temperature, humidity, wind speed and others. The solar global radiation is collected by Silicon Pyranometer Smart Sensor, Manufacturer: Onset, Bourne, MA, USA, whose main specifications are:

-

Measurement range: 0 to 1280 W/m²; spectral range: 300 to 1100 nm; accuracy: typically, within ±10 W/m² or ±5%, whichever is greater in sunlight; additional temperature induced error ±0.38 W/m²/°C from 25 °C; resolution: 1.25 W/m²; operating temperature range: −40 °C to 75 °C; manufacturer: Onset.

The air temperature is collected by a temperature sensor, whose main specifications are:

-

Measurement range: 40 °C to 100 °C; accuracy: ±0.25 °C from −40 °C to 0 °C, ±0.20 °C from 0 °C to 70 °C; resolution: 0.02 °C; manufacturer: Onset.

A digital system for the daily registration of restaurant users, whether students, teachers or other employees, storing information on the number of people in the restaurant at each operating time. As for electricity consumption, the building’s electricity meter was used, allowing the construction of an hourly database of electricity consumption and the number of users.

As the building being analyzed is a public building, it was necessary to search for reference values for the purchase of materials and services of this nature from Brazilian public administration bodies on the Brazilian Federal Government’s purchasing platform [29]. The simulated values were estimated according to a public tender held by UFERSA itself. The financial analysis model uses the following variables for all scenarios: fuel cost, fuel cost adjustment, inflation rate, discount rate, and useful life of the project.

3.1.2. Fuel Cost

The fuel cost or electricity price corresponds to the value of the energy bill paid divided by the value of active consumption in the period. The value used was calculated based on the average of the base period according to Equation (1), resulting in 0.1210 USD/kWh. It is noteworthy that all monetary values in this article were used with the parity of the Brazilian real (BRL) to the US dollar of BRL 5.13 to USD 1.00.

$$\mathrm{CC}={\displaystyle \frac{\mathrm{Vp}}{\mathrm{Ca}}}$$

(1)

where

CC—is the fuel cost [USD/kWh];

Vp—is the amount paid [USD] and

Ca—is the active consumption [kWh].

3.1.3. Adjustment of Fuel Consumption

Periodically, the prices of products and services consumed by the population undergo corrections. In energy generation, transmission and distribution, the end consumers notice this adjustment in the value of the energy tariff charged and, in each sector (residential, industrial, public agencies, others), the corrected value may be different.

To determine the appropriate percentage of readjustment, also known as the escalation rate, necessary for the simulation, the methodology indicated in the Life Cycle Costing Manual, developed by the National Institute of Standards and Technology (NIST) of the government of the United States, was used [30]. To determine the nominal escalation rate, it is first necessary to calculate the nominal price escalation, using (2).

$$C_t=C_0\times {\left(1+E\right)}^t$$

(2)

where

C_t—the actual (nominal) cost of a particular commodity as of some future date, in this case of electrical energy;

C₀—the cost of that commodity as of the base date;

t—is the number of periods between the base date and the date that the cost is incurred, which can be in days, months or years and

E—the nominal price escalation rate.

If the nominal rate of price escalation, E, for a commodity, is different from the general rate of inflation, then a real (differential) rate of escalation, e, should be computed using (3).

$$e={\displaystyle \frac{1+E}{1+I}}-1$$

(3)

where

e—is the real rate of escalation and

I—is the inflation for the period; in Brazil, this rate is determined through the National Consumer Price Index (IPCA).

3.1.4. Inflation

Inflation is the term attributed to the increase in prices of products and services consumed by the population, which is calculated by price indexes, according to the Brazilian Institute of Geography and Statistics (IBGE), the Brazilian body responsible for measuring and publishing this index. In Brazil, the index that measures inflation is called IPCA, the Broad National Consumer Price Index, which measures the variation in prices of products and services for families with monthly income between 1 and 40 minimum wages [31].

Data relating to year-to-year energy tariff values can be found in the virtual database of the Brazilian Electric Energy Agency (ANEEL) [27], responsible for regulating the electricity sector. As this research has a public institution in the state of Rio Grande do Norte as its object of study, only data referring to the Public Agencies sector of the electricity company that serves the state were collected, starting in 1996, disregarding the taxation percentages.

The IPCA historical series since 1996 is available on the IBGE website, but the value entered in the corresponding field on RETscreen 4.0 was calculated using the accumulated result of the last 12 months, counting from April 2024, a value that financial institutions adopt as an inflationary reference.

3.1.5. Discount Rate

The most used method in project investment analysis is the net present value (NPV), which consists of the concentration of expected cash flow values at the initial date and discounted by the opportunity cost of those who invested resources in the project. This discount rate is also known as the minimum attractiveness rate of return (MARR) [32]. From another angle, the MARR can be understood as the minimum rate established by the organization at which the project becomes economically viable compared to the application of this resource in another investment that obtains a return in this established percentage. The discount rate applied will be 8% per year, the same as specified in the Brazilian National Energy Plan (PNE) 2030, which is also used by electric utilities in public calls for Brazilian Energy Efficiency Program (PEE) projects [33].

3.2. Energy Performance Indicators (EnPIs)

To determine the EnPIs, the study boundary was taken into consideration and delimited according to the results obtained from the significant energy use, identified through the installed load audit. For this research, two EnPIs were calculated, one based on area and the other on occupation. These were based on the definitions of ISOs 50001 [34] and 50006 [35], as well as other reference authors, such as [36,37]. These indicators can point out the reference values for the building’s energy efficiency and enable comparison with benchmarking, organization and buildings with similar characteristics.

Indicator based on active energy consumption according to the useful built area of the building. Calculated using area data (in m²) from the building’s architectural design plans and the historical series of active energy consumption (kWh) measured monthly, according to (4).

$$E_{m/year}={\displaystyle \frac{P}{a}}$$

(4)

where

E_m/year—is the indicator [kWh/m²/year];

P—is the active energy consumed in the period [kWh] and

a—the useful area of the building [m²].

The energy consumption indicator per building user, calculated according to the number of building users in the measurement period and the active energy consumption (kWh), is presented in (5).

$$E_{u/year}={\displaystyle \frac{P}{u}}$$

(5)

where

E_u/year—is the indicator [kWh/users/year];

P—is the active energy consumed in the period [kWh] and

u—is the number of users in the period.

3.3. Statistical Analysis

ISO 50006 [35] advises that the IDE is calculated using energy consumption data and relevant variables and that the model validity tests use statistical tests such as the p-value, F-test or coefficient of determination. The coefficient of determination, R², and Pearson’s correlation coefficient, r, were determined to identify the relationship between electricity consumption, the dependent variable and other variables monitored over time. R² is a value that varies from 0 to 1, the closer to 1 the more correlated the variables are, demonstrating the adequacy of the model, in other words, how much the data variability explains the model [36]. Pearson’s r determines the linear correlation between the variables and demonstrates the degree of strength of the relationship between the variables. The r varies between −1 and 1, the closer to these extremities the stronger, negative values when inversely proportional and positive when directly proportional. The coefficient values can be classified as follows according to [38]: r = 0.10 to 0.30 (weak); r = 0.40 to 0.60 (moderate); and r = 0.70 to 1 (strong).

3.4. NZEB and PEB Classifications

Annex D of the INI-C methodology addresses the minimum requirements for a building of a prominent nature to be considered an NZEB and PEB. The energy generation potential (PG) of the building according to INI-C is determined by Equation (6), and for classification, if PG is greater than 50%, it is an NZEB, if it is greater than 100%, it will also be a PEB.

$$PG={\displaystyle \frac{G_{ee}\times f_{ce}}{\left(C_{eereal}\times f_{ce}\right)+\left(C_{etreal}\times f_{ct}\right)}}$$

(6)

where

f_ce—electrical energy conversion factor;

f_ct—thermal energy conversion factor;

C_eereal—real electrical energy consumption [kWh/year];

C_etreal—real thermal energy consumption [kWh/year] and

G_ee—electricity generation [kWh/year].

4. Results

Knowing the main scope of the RU, based on on-site inspections, it was identified that the main energy sources used for the full operation of the building are electricity and liquefied petroleum gas (LPG), the latter fuel being used only for the preparation of meals, while all other processes of transformation of energy into work are carried out using electricity. In addition, the kitchen, where the LPG is used, has an exhaust system, and the environment is not adjacent to the cafeteria, reducing the impact of the heat generated by the LPG. Therefore, the use of electricity in the RU building was determined as the frontier of this research.

The building’s activities began in August 2018, with the installation of energy recording equipment consumed in July 2019. Furthermore, the building was closed for two years, from March 2020 to March 2022, due to the COVID-19 pandemic. Electrical equipment such as that installed in the building loses its efficiency without use or even stops working due to depreciation, which can cause a change in the consumption profile before and after the shutdown period. Therefore, the baseline period was chosen from the start of post-pandemic UR activities, from March 2022 to March 2024.

Having determined the scope and boundary of this study, a survey of the building’s installed load was carried out to investigate the significant energy use (SEU). The building’s SEU results are seen in Figure 3.

Refrigeration, including food and beverage refrigeration, cold rooms and air conditioning, uses energy that corresponds to 55.41% of the building’s installed load. The power observed for heating is distributed across appliances such as electric ovens, dishwashers and bain-marie food distribution tables. As for the power classified as “Others”, which appears as the second highest installed load potential, there is a range of essential equipment for preparing meals and for the comfort of employees who provide services, such as industrial blenders, vegetable peelers, extractor fans, notebooks, printers and others. As for the building’s lighting, internally and in closed environments around the building, they are mainly composed of 20 W LED tube lamps measuring 1.2 m, 6500 K, distributed in luminaires for two lamps each; in the external lighting, reflectors with sodium vapor bulbs lamps with 150 W ballasts and automatic activation by photocells are used.

Figure 4 shows the curves of average temperature and average global radiation from March 2022 to March 2024.

It can be seen from the graph that both data curves are directly related to each other. This data becomes very relevant when thinking about energy efficiency techniques aimed at reducing electrical energy consumption.

As demonstrated in the SEU graph (Figure 3), the largest power in kW of equipment is for refrigeration, which makes it possible to infer that the ambient temperature influences energy consumption in a directly proportional way. Based on this, the active energy consumption curve in the baseline period was crossed against the temperature graph for the same period, the results are seen in Figure 5.

From the graph, the active energy curve resembles the air temperature, proving the hypothesis that due to the amount of load installed with refrigeration equipment and the characteristic climate, the temperature causes energy consumption to be high or decreased. It should be noted, however, that in some months an inverse relationship is perceived, as in March and April 2023, which can be explained by the complications in the use of some equipment throughout the baseline period, mainly the air conditioning devices in the cafeteria.

The directly proportional relationship between the temperature and air conditioning consumption occurs because the heat exchange with the external environment and the constant entry and exit of users means that the compressors of these equipment must work much harder to reach the operating temperature. Setpoint is the temperature value of the device at which the compressor is in standby state. This relationship between the number of users influencing consumption can be seen in the graph in Figure 6. It is important to highlight that the restaurant only operates during the school days established in the academic calendar, from Monday to Friday with lunch and dinner provided, and on Saturdays, only lunch. Therefore, in some months, there is a drastic reduction in the number of users due to the academic recess period starting and ending throughout the month, as is the case between June and July 2022, when the academic semester started on the 25th and leading to a very high volume of users in the last week of the month. The correlation between the two curves appears weaker compared to the temperature; in the graph in Figure 6, the moments in which inverse action between the variables were recorded are highlighted in yellow.

The graph in Figure 6 also shows an increase in active consumption from October 2022 onwards. During that month, all the cafeteria’s air conditioning units, which had been stopped due to technical problems, returned to service. This, combined with high-temperature levels, contributed to consumption even with the drop in the number of users. The same hypothesis could explain the discrepancy between November 2022 and January 2023.

Figure 7 shows the graph of global radiation during twenty-four hours on 2 July the highest value during that month.

The month of October presents a peak of 1279 W/m², a value that is recorded several times throughout the month, in addition to the fact that the daily radiation curves have fewer drops, which infers hours of prolonged sunshine and less cloudy days. Figure 8 shows one of the days on which the peak value was recorded.

The high and constant levels of radiation incidental to the way in which the building is constructed are favorable for the installation of a photovoltaic solar plant, even considering generation losses due to high ambient temperatures. Based on the graphs presented, the SEU, the understanding of the scope of the building studied and some relevant variables that affect consumption in the RU were determined: temperature (°C) monthly average, global radiation (W/m²) monthly average and number of users. Furthermore, a static factor was observed that also influences energy consumption, the days of the week due to the weekly menu, and the distribution of classes. It is defined as a static factor as it presents little or no variation in the standard during the academic calendar. The weekly menu throughout the month, despite being varied, tends to be repeated, concluding that it is expected that on days with a greater flow of people, the amount of electricity will be high. Furthermore, classes at the university are accumulated over three days, Tuesday, Wednesday, and Thursday, and on the other days, the distribution of academic activities is reduced. Figure 9 illustrates the relationship between days of the week and users, demonstrating the number of meals served on each day of the week recorded by the university system.

After observing the data, a statistical correlation analysis was studied between the relevant variables and the total active energy consumption of the base period, the results are seen in

Table 4. Statistical analysis.

Method	Temperature	Radiation	Number of Users
Pearson correlation coefficient—r	0.7845	0.4127	0.5184
Coefficient of determination—R2	0.6155	0.1693	0.2687

.

By calculating the correlation coefficients, temperature has an impact on the total active energy consumption of the restaurant. According to Table 4, temperature corresponds to the highest R² value, in addition to having r considered “strong”. On the other hand, radiation and the number of users presented a “moderate” r, both with R² with a determination that demonstrates a weak direct relationship.

Regarding the impact of the number of users on electricity consumption, presented in Table 4, it may indicate the inadequacy of the current air conditioning technology in relation to the behavior pattern of the building’s occupancy and use. Due to the characteristic of being very rotating and with variations in the simultaneous number of people, using a solution that operates at variable speed, such as equipment with variable refrigerant flow (VRF) technology, can be an appropriate energy efficiency measure. This type of equipment maintains the internal temperature more efficiently, as the compressor is on uninterruptedly with power variations; therefore, it can identify the temperature variation with greater precision to the detriment of the setpoint, making it more effective in identifying the instantaneous thermal load.

Using the EnPI from Equations (4) and (5), the values of 80.78 kWh/m²/year and 0.88 kWh/user/year were obtained. Other authors used the same or similar indicators in university archetypes, as in [8,36].

Although ISO 50006 considers EnPI kWh/m²/year in this way, as several authors use indicators based on the built area, this indicator was also calculated to validate the data. Other studies calculated the EnPI according to the number of students and this research calculates the EnPI according to the users, as already explained, the majority of whom are undergraduate students, so for comparison purposes the results can be compared. The result of this comparison with other studies can be evaluated in Figure 10.

In Figure 11, a comparison is presented between the electrical energy consumption for each meal served in restaurants and university canteens. It is worth highlighting for the latter that its physical constructive aspects are like the URs given their proportions. The figure shows a favorable response, UFERSA presents a good performance in comparison with peers, among the studies analyzed, only two had a lower consumption, and the others were greater than or equal to 1.0 kWh/meal.

The Brazilian Council for Sustainable Construction (CBCS) provides an online platform for measuring and qualifying the performance of various types of benchmarks, including public buildings and restaurants [42]. The indicator is then classified as “Efficient”, “Typical” and “Inefficient” according to the reference values seen in

Table 5. CBCS reference values.

CBCS Restaurant Typology (kWh/m2/year)
Efficient	Typical	Inefficient
Up to 62.95	Between 62.95 and 70.73	Above 70.73

. Using the mentioned platform fed with the collected data and using the closest city option available, Apodi/RN, the calculated value was 80.78 kWh/m²/year, corroborating the value found.

The platform considers in its calculations the envelope, power density, lighting power density, the type of air conditioning, the environment and the corresponding technology, the physical area and the type of restaurant, whether it is à la carte or self-service, what meals they serve, in addition to considering the climatic aspects closest to the city in which the building is located. Benchmarking was simulated with complete data from 395 buildings covering 25 states in Brazil. Based on the reference values, the building studied is classified as “Inefficient”. Given this, the need to adapt the building becomes evident, and the result obtained is a warning that the building needs some energy efficiency strategy in its envelope, reducing the thermal load and heat absorption, in equipment or a systems retrofit.

As studied in the SEU graph and the analysis of consumption as a function of temperature (graphically and statistically), the use of air conditioning has a very marked influence on the building’s energy consumption. The devices were installed at the same time as construction was completed, seven years ago, a relatively short time considering the useful life of this type of equipment, which can reach 20 years, according to [43]. However, as already mentioned, the devices were broken for a portion of the baseline period, which signals that their performance is less than ideal. It was also mentioned that the restaurant was closed for two years due to the COVID-19 pandemic, so lack of use could cause damage to the equipment for various reasons such as loss of lubrication, leaks and aging of the refrigerant fluid.

Scenario 1 was simulated to improve the building’s energy consumption by replacing existing low-performance air conditioners with better equipment with the same installation characteristics to avoid adaptation costs, according to

Table 6. Technical characteristics of simulated substitutions.

Scenario	Reference Case	Proposed Case
1.1	COP 3.06, Cooling power 4.60 kW	IDRS 5.53, Cooling power 5.091 kW
1.2	COP 3.34, Cooling power 1.125 kW	IDRS 7.62, Cooling power 1.08 kW
1.3	COP 3.24, Cooling power 0.815 kW	IDRS 7.60, Cooling power 0.813 kW
2	U = 1.64 W/m2. K	U = 1.348 W/m2. K
3	U = 6.52 W/m2. K and 5.55 W/m2. K	U = 3.185 W/m2. K and 0.557 W/m2. K

. Conventional equipment was replaced by inverter technology considered suitable for detecting variations in thermal load due to the variable number of users. In [44], a comparative study was carried out between conventional and inverter technologies, the results of which showed savings of between 30% and 40% for the former; in addition, it was proven that depending on the thermal load inside the environment, energy consumption showed a higher demand and the inverter device demonstrated superior performance in the cycle studied, reducing operations to maintain the appropriate temperature at times of greatest thermal load. The researchers in [44] obtained a similar result, in which the estimated savings were almost 30%. Scenario 1 consists of three sub-scenarios according to the power of the equipment: 1.1 cassette split type 48,000 BTU in the cafeteria, 1.2 split high wall 12,000 BTU in the pantry, and 1.3 split high wall 9000 BTU in the other rooms.

Scenarios 2 and 3 are solutions based on the environment and climate of the restaurant’s region, the semi-arid region. The proposition of a thermal blanket below the roof (scenario 2) and the exchange of single glazing for insulated glazing (scenario 3), also called double glazing, minimize the incidence of external heat into the UR, which enhances the use of the existing air conditioner, as it will make it reach the setpoint temperature in a reduced time and remain there for a prolonged period, consequently reducing consumption. The technical characteristics of each simulated replacement are seen in Table 6.

By associating the energy efficiency actions from scenarios 1 to 3, scenario 4 was created, since, according to [16], the interaction of the benefits of each action results in performance that needs to be evaluated as a new energy efficiency action.

To develop the simulation, the electricity cost of 0.1210 USD/kWh was used; inflation according to the IPCA accumulated in the last 12 months at the time, corresponding to 3.93% per year; the discount rate or minimum attractiveness rate (MAR) equal to 8% per year; the period adopted was 25 years, which is the same period that photovoltaic module manufacturers offer as a production guarantee. Regarding the rate of escalation in the price of fuel and electricity,

Table 7. Average tariff for the public power sector and IPCA in the years 1996 to 2023.

Year	Inflation (%)	Accumulated Inflation	Average Tariff (USD/MWh)	Nominal Price Escalation Rate (%)	Real Rate of Escalation (%)	Accumulated Nominal Price Escalation Rate
1996	9.56	1.000	19.89			1.00
1997	5.22	1.052	21.05	5.80	0.55	1.06
1998	1.65	1.070	22.19	5.45	3.73	1.12
1999	8.94	1.165	23.25	4.76	−3.84	1.17
2000	5.97	1.235	26.40	13.55	7.15	1.33
2001	7.67	1.329	29.90	13.25	5.19	1.50
2002	12.53	1.496	34.75	16.23	3.29	1.75
2003	9.30	1.635	41.04	18.08	8.04	2.06
2004	7.60	1.759	49.95	21.73	13.13	2.51
2005	5.69	1.860	54.25	8.60	2.75	2.73
2006	3.14	1.918	56.90	4.88	1.69	2.86
2007	4.46	2.003	61.05	7.30	2.72	3.07
2008	5.90	2.122	56.48	−7.49	−12.65	2.84
2009	4.31	2.213	56.41	−0.13	−4.26	2.84
2010	5.91	2.344	61.99	9.90	3.77	3.12
2011	6.50	2.496	62.01	0.03	−6.08	3.12
2012	5.84	2.642	65.84	6.18	0.32	3.31
2013	5.91	2.798	54.93	−16.57	−21.23	2.76
2014	6.41	2.978	61.10	11.23	4.53	3.07
2015	10.67	3.295	69.88	14.38	3.35	3.51
2016	6.29	3.503	75.76	8.42	2.00	3.81
2017	2.95	3.606	77.54	2.34	−0.59	3.90
2018	3.75	3.741	89.25	15.10	10.94	4.49
2019	4.31	3.902	92.81	3.98	−0.31	4.66
2020	4.52	4.079	100.32	8.10	3.42	5.04
2021	10.06	4.489	121.16	20.77	9.73	6.09
2022	5.79	4.749	132.22	9.13	3.15	6.65
2023	4.62	4.968	129.73	−1.88	−6.21	6.52

was developed.

By taking the average of the values in the real rate of escalation column (e), the value of the escalation rate for the period is obtained, resulting in 1.27% per year. From the graph in Figure 12, which compares the accumulated inflation and the accumulated nominal price escalation rate, without correction for inflation, the value of the average tariff paid by the public agencies sector follows growth close to inflation with little real gain on the part of the electricity utility.

The simulated actions,

Table 8. Simulation result.

Scenario	Initial Investment (USD)	Payback (Years)	IRR	Net Annual Reduction in GHG Emissions
				Unconsumed Liters of Gasoline	tCO2	Cost (U/tCO2)
1	25,750.77	2.80	36.90	2621.00	6.10	1273.98
2	1309.05	0.70	144.70	559.00	1.30	1569.69
3	10,410.05	6.20	17.20	473.00	1.10	924.91
4	37,470.07	3.30	31.50	3266.00	7.60	1204.64

, were evaluated based on the initial cost of the investment; payback, which is the time necessary for the income obtained from applying the action to reach an amount equal to the amount invested, meaning that from that moment onwards any income becomes profit; and the internal rate of return (IRR), defined as the rate at which an investment is recovered through the income obtained by the project itself [32]. In simplified terms, it is the rate at which the analyzed investment would convert the investment into income. This indicator needs to be compared to the opportunity cost or the minimum attractiveness rate (MARR), because in the case of an IRR greater than the MARR, then the project in question is more profitable than the investment opportunity that brings a return equal to the MARR; in case of a lower IRR, then the project is less profitable than the MARR.

The Grid-Connected Photovoltaic System (GCPVS) has gained a lot of market space in Brazil and around the world, and the cheaper technology over the last decade has made it increasingly accessible. The installation of some type of renewable energy source does not constitute an improvement in energy performance within the scope and boundaries of the EMS [34]. Although the installation of a renewable source, such as photovoltaics, brings numerous benefits, mainly environmental, this action does not change the energy consumption related to the use of energy because of the planning and actions determined in the EMS; therefore, there will be no measurable improvement in energy consumption or energy efficiency in the UR.

Keeping in mind the installation cost and the energy potential of the region, a GCPVS was designed to meet the UR’s active energy consumption demand, with its components presented in Table 2. The plant would have an installed capacity of 48.84 kWp, and 111 modules of 440 Wp are required.

The simulated value of the GCPVS as well as the equipment were based on the results of the bidding carried out by the university to acquire the system, in which the value approved in the process was USD 22,986.27. Throughout the contract, the company requested economic–financial rebalancing, whose kWp value jumped to USD 644.56. Therefore, by multiplying the adjusted kWp value by the installed power of the system, the value of the scaled project would total the value of USD 31,480.51.

To calculate the savings over time, it was necessary to observe the changes to the law on microgeneration and distributed mini generation in Brazil, Law nº 14,300/2022, this regulation defined application group criteria and charging percentages on the use of the transmission and distribution grid in increasing percentages from 2023 to 2028, with openings for changes from 2029 onwards, on B wire (one of the components of the use of the distribution system—TUSD) as shown in

Table 9. GCPVS savings before and after the new rules of law nº 14,300.

Time (t)	Year	Percentage	B Wire (USD)	Old Rule (USD)	Current Rule (USD)
0	2022	-	-	9923.76	-
1	2023	15%	0.0436	10,023.00	9658.37
2	2024	30%	0.0872	10,123.23	9384.64
3	2025	45%	0.1307	10,224.46	9498.72
4	2026	60%	0.1743	10,326.70	9346.62
5	2027	75%	0.2179	10,429.97	9189.12
6	2028	90%	0.2615	10,534.27	9026.11
25	2047	90%	0.2615	12,726.55	10,904.52

. In the case of the proposed study, in which the system would be installed after the new rules came into effect and the system power is less than 500 kW, the plant would be considered as “GD-II” in which charging would occur as narrated.

In addition to this regulation, the analysis of monthly distribution and savings throughout the year was carried out following the rules of ANEEL Normative Resolution N° 1059/2023 for the high voltage group A4, hourly rate, in which the kWh value compensated during peak hours and that was generated during off-peak hours, needs to be multiplied by the adjustment factor [27].

Table 10. GCPVS simulation.

Initial Investment (USD)	Payback (Years)	IRR	Net Annual Reduction in GHG Emissions
			Unconsumed Liters of Gasoline	tCO2	Cost (USD/tCO2)
31,480.51	3.70	26.80	3094.00	7.20	790.00

presents the results of the GCPVS simulation. A competitive cost in reducing GHG emissions and a greater reduction in CO₂e emissions among all scenarios are observed. The payback period is 3.7 years, and the IRR is three times the MARR, although the initial investment is higher than in scenario 4.

In the Brazilian scenario, a common concern about the distributed generation (DG) is the viability of investment after the changes made to the regulatory framework for micro- and mini-distributed generation and its energy credit compensation system. To assess the impact of changes in the case study, Table 9 was prepared by comparing the value of savings generated each year over the estimated useful life of a photovoltaic system, that is, the difference between the energy bill without DG and with DG before and after the new regulation. It is worth mentioning that the values projected for future years were made taking the adjustment escalation rate as 1.27%.

The current legislation did not determine how the rules for the GD-II group will be applied from 2029 onwards, so the maintenance of B wire charges at 90% was used for the calculations. It should be noted that given the lack of definition of the rules for the GD-II group from 2029 onwards, even using a conservative value, in this case 90%, the simulated values may be different, reducing or increasing the rate of return and consequently the accumulated cash flow. The difference over the years increases as the rate percentage grows. In the last simulated year, the difference is approximately two thousand dollars between the savings that the institution would have had if the installation had been carried out before the new rules came into effect. This difference is also highlighted when analyzing the projection of the accumulated cash flow simulated in RETScreen for the sized photovoltaic system presented in

Table 11. Accumulated GCPVS cash flow before and after law nº 14,300.

Year	Old Rule (USD)	Current Rule (USD)
0	−31,282.65	−31,282.65
1	−20,644.64	−22,890.25
2	−10,006.43	−14,498.05
3	−631.77	−6105.65
4	11,269.79	2286.74
5	21,907.99	10,679.14
6	32,546.00	19,071.54
25	234,670.37	192,000.78

. There would be little change in the payback time between the two, before the law it would be 2.9 years and an IRR of 34%; however, the accumulated cash flow demonstrates a considerable difference in profitability at the end of the studied life cycle.

Despite the loss of financial profitability caused by the B wire tariff, it is still possible to state that investing in the scaled GCPVS would be a viable alternative to continuing to pay the electricity bill to the electricity utilities. In the graph in Figure 13, generation was estimated in comparison to consumption over a year, taking the year 2023 as a base. In the graph, in some months, consumption is greater than generation and, in others, this is reversed, as is predictable according to the consumption profile and radiation throughout the year. The billing cycles were designed so that credits from the months with higher generation can be used in other months in which consumption is higher, thus mitigating the need for a plant with greater installed power and consequently more expensive.

According to Equation (6), for using the scaled GCPVS data and consumption for the year 2023, a PG of 1.0025 or 100.25% was determined, which gives the restaurant the NZEB classification. The same INI-C methodology also contains the requirement to be considered a PEB, for that the PG result must be greater than 100%, a criterion that was also met and can thus be classified as both an NZEB and a PEB [45]. Also using INI-C the envelope of the studied building was classified as “A”, which met the criteria to be classified as an NZEB and PEB [46], obtaining a similar result as studying a preschool building, obtaining an “A” classification for the envelope, lighting and in general, with a PG of 100.5%, giving the building NZEB and PEB status, and a reduction in CO₂ emissions by 30%.

Analyzing the data recorded on the investing platform regarding the value of carbon credits traded internationally, it is possible to extract the average value traded during the entire baseline period of this study, EUR 80.71 or USD 87.11.

The simulations carried out regarding energy efficiency actions and the photovoltaic system connected to the grid together total an annual reduction of 14.8 tCO₂e. Based on the premise that all efficiency actions were complied with as soon as the photovoltaic system was installed, it would be possible to obtain extra passive profitability annually through the sale of Greenhouse Gas Emission Reduction or Removal Certificates. As each ton of carbon dioxide is equivalent to one certificate unit, the annual revenue from this source would be USD 251.31 for the UR.

This amount sold can be added to the accumulated cash flow of the projects, reducing the payback, which would increase the IRR, making investments even more attractive from a financial point of view. Furthermore, these monetized values can be enhanced when the social aspect is introduced. There are standards for evaluating and monitoring GHG reduction or removal projects that appreciate the co-benefits arising from these projects. Co-benefits are gains that go beyond emission reductions and are associated with the Social Development Objectives. Among the types of projects that present these characteristics, some invest in education and encourage local cultures, and others support health institutions and sports [24].

Therefore, the notable social benefits in the scope of teaching, research, extension, culture and sport would enable the environmental initiative to generate co-benefits. The impact of this is perceived in the interest of certificate applicants, who prefer initiatives that also encourage the improvement of local social development by increasing the added value of the certificates generated by it [24].

5. Conclusions

The results of the simulations are promising. In the replacement of air conditioning equipment, scenario 1, the calculated payback indicates that the investment would be paid off in less than three years of operation, when compared to the useful life of the equipment, which can be considered a viable return time. The internal rate of return, IRR, was 36.90%, more than four times the value of the minimum attractive rate, MARR, set at 8%.

Scenario 2 is the most economically viable; the results were above the average of the other scenarios with an IRR of 144.70% and a payback time of less than one year, and thus representing an excellent alternative for semi-arid climate regions, increasing the thermal comfort of the environment, in addition to reducing the consumption of electrical energy used to reduce the temperature of the environment.

As for scenario 3, this presented the longest payback, 6.2 years. The IRR found was 17.20% (2.15 times higher than the MARR), which financially justifies the investment, especially when compared to the useful life of a window. Furthermore, replacing windows is a solution that encompasses both thermal comfort and visual comfort, as it preserves the incidence of light while reducing the entry of heat through radiation, which means that the number of lamps on during the day is lower and the air conditioning will consume less electrical energy to reach and maintain the temperature.

Scenario 1, in addition to being economically attractive, also presented the greatest reduction in emissions, 6.1 tCO₂e. This is due to the double emission of greenhouse gases (GHGs) that occurs when using the air conditioning, which emits pollutants, as it uses electrical energy to operate. Depending on the generating source, this emission may be higher, as well as the use of HFC refrigerants (hydrofluorocarbons) used in the process are also sources of GHG emissions into the atmosphere. Therefore, scenario 1, from an environmental point of view, is favorable. Compared to scenario 3, this scenario resulted in the lowest cost per ton of CO₂e. Another benefit of this scenario is the conservation of the air conditioning system from the beginning of its operation, due to the joint action with the thermal blanket and double-glazed windows, reducing the thermal load inside the building, decreasing the need for corrective maintenance throughout the system’s useful life.

Compared to the investment in scenarios 1 to 3, a 48.84 kW_p GCPVS proved to be an economically and environmentally attractive investment, with a payback of 3.7 years and an IRR of 26.8%, in addition to a reduction in emissions of 7.2 tCO₂e.

Using the INI-C assessment method, with the installation of the GCPVS, a generation potential value of 100.25% was calculated, and thus the university restaurant on the Caraúbas campus would be classified as an NZEB and a PEB, not only due to the positive balance between electricity inputs and outputs, but also due to the reduction in GHG emissions.