Water heating in Brazilian residential buildings: a comparison between electric showers and solar heating

Aquecimento de água em edificações residenciais brasileiras: comparação entre chuveiros elétricos e aquecimento solar

Abstract

This study compares electricity consumption between solar water heating systems and electric showers in twelve bioclimatic zones in Brazil. Solar heating was assessed using the simplified and detailed methods of INMETRO's Normative Instruction for Residential Buildings (INI-R) and the Energy Plus programme. At the same time, electric showers were analysed using programming routines. Annual energy consumption for water heating varied between 143 and 1825 kWh for electric showers and between 0 and 539 kWh for solar heating systems, with differences in the results depending on the methodology used for evaluation. The economic viability of solar heating depends on the climate and the local energy tariff, being advantageous only in Canela, Vitória da Conquista, Brasília, Rio de Janeiro, Goiânia and Cuiabá. The study highlights the need for more research on the subject and recommends that INMETRO reconsiders adopting the simplified method, as it presents more favourable results for certification than the detailed method.

Keywords
EnergyPlus; Sustainability; Residential buildings

Resumo

Este estudo compara o consumo de eletricidade entre sistemas de aquecimento solar de água e chuveiros elétricos em doze zonas bioclimáticas do Brasil. O aquecimento solar foi avaliado por meio dos métodos simplificado e detalhado da Instrução Normativa do INMETRO para Edificações Residenciais (INI-R) e do programa Energy Plus, enquanto os chuveiros elétricos foram analisados por meio de rotinas de programação. O consumo anual de energia para aquecimento de água variou entre 143 e 1825 kWh para chuveiros elétricos e entre 0 e 539 kWh para sistemas com aquecimento solar, com diferenças nos resultados conforme a metodologia utilizada para avaliação. A viabilidade econômica do aquecimento solar depende do clima e da tarifa local de energia, sendo vantajosa apenas em Canela, Vitória da Conquista, Brasília, Rio de Janeiro, Goiânia e Cuiabá. O estudo destaca a necessidade de mais pesquisas sobre o tema e recomenda que o INMETRO reavalie a adoção do método simplificado, pois ele apresenta resultados mais favoráveis à certificação do que o método detalhado.

Palavras-chave
EnergyPlus; Sustentabilidade; Edificações residenciais

Introduction

Residential buildings account for 27.5% of the Brazilian national electricity consumption (EPE, 2024) and over one-third of the global final energy consumption when accounting for building and operation (IEA, 2025). There is, therefore, an interest in reducing the electricity consumption of this building typology nationally through energy-efficient systems or projects that follow the Brazilian bioclimatic guidelines. In this context, the work of Teixeira et al. (2022) is important for analysing the focus of energy efficiency assessments. The authors used data from Eletrobrás (2019) on the stock of Brazilian residential buildings to understand the share of energy consumption of various building subsystems. The two subsystems with the most significant electricity impact on household consumption are refrigerators and water heating, according to Teixeira et al. (2022). Therefore, studies focusing on reducing these appliances' electricity consumption are critical to the sustainability of Brazilian electricity production.

The Brazilian federal government has been constantly researching household water heating, with programmes that reduce electricity consumption (Brazil, 2023; Euclydes et al., 2022; MME, 2024). Among the possible technologies, replacing electric showers with solar heating has been widely proven to reduce electricity consumption and eliminate the peak demand at night hours, which hinders the sustainable production of electricity and makes it more expensive. One example of a Brazilian public project on solar water heating is Pronasol, a national incentive programme for solar heaters in homes (Brazil, 2023). The bill is under discussion by the Brazilian government and shows an interest in incentivising such technology.

However, despite the possible incentives, the design and operation of solar water heating demand knowledge and the comprehension of the variables influencing water heating, which are complex and vital to its efficacy. As a key reference document, the Brazilian National Institute of Metrology Standardization and Industrial Quality (INMETRO) presents the Normative Instruction for Residential Buildings (INI-R), which is the most up-to-date initiative for labelling and predicting buildings' energy consumption (INMETRO, 2022). Concerning water heating, the methodology presents equations that estimate the reduction in electricity consumption by adopting alternative systems (solar heating, gas heating or other possibilities) to the reference system (electric heating). The variables used in the model include climatic conditions (dry bulb temperature, solar irradiation and water temperature), usage conditions (shower temperature and schedules) and operational conditions (water storage temperature, heat treatments for Legionella, among others).

Another possibility for assessing solar water heating performance is using computer simulations to understand the thermal balance exchanges (Booysen et al., 2019; Santos; Giglio, 2020; Vieira; Beal; Stewart, 2014). Such is the example of using the EnergyPlus programme (Crawley et al., 2001) to assess the water heating demands throughout the day, including thermal gain and losses and the electric backup consistency if no solar energy is available. Studies such as those of Vieira, Beal and Stewart (2014), Sangoi and Ghisi (2019) and Pang and O’Neill (2018) use the EnergyPlus modelling as an alternative towards understanding the solar water heating thermal fluxes.

In such a context, optimisations are also researched in the context of electric showers. The studies of Sangoi, Scolaro and Ghisi (2023), Naspolini and Rüther (2019), Vaz et al. (2023) and Kenway, Scheidegger and Bader (2019) are examples of comparisons of electric showers and alternative systems, including feasibility and dynamic modelling of the electrical processes. Electric showers are interesting systems with a high-efficiency rate, small installation cost and easy operation. However, all authors point out the necessity of reducing peak-time active power demand, as most electric showers are used at night (18:00~22:00) when most household appliances are in use. Therefore, studies on solar water heating highlight the potential for using solar energy as a viable and feasible alternative, especially on the country scale.

This research aims to compare the energy consumption of water heating systems by evaluating them using the Brazilian Normative Instruction for Residential Buildings (INI-R) available methods (simplified and detailed) and computer simulation via EnergyPlus. Applying both methodologies to cities in Brazil's bioclimatic zones seeks to extend knowledge about the potential for applying solar heating and reducing energy consumption. As its main contribution, the study presents the amount of electricity used to heat water evaluated using the methods available in Brazil. It also includes a feasibility assessment of solar water heating systems, further expanding the knowledge about the viability of using solar water heating in different Brazilian cities. This information can help designers understand the scale of the impact of this subsystem, help the government revise the INI-R, and help promote more sustainable water heating systems in the country. Studies on this subject are scarce in Brazil, and the goal is to expand the state of the art by applying a theoretical comparison that may be foundational to understanding the intranational variation of solar water heating efficiency.

Materials and methods

First, one needs to contextualise the cities chosen for the assessment. Then, procedures were explained in simulating the thermal processes of electric showers and solar water heating. Finally, the feasibility assessment was presented, including all the parameters and assumptions for the comparison. One aimed at understanding the maximum value a solar water heating system may cost to be feasible in each location assessed. Figure 1 shows the steps considered in the study.

Figure 1
Diagram of the processes executed in the study

Object of study

The object of study is a social housing unit designed with an equivalent function to provide sufficient energy for heating water for four people. One used the building models from the HabLabEEE project (LabEEE et al., 2024) as a reference for defining the floor plan, including the water heating systems characteristics, such as the location of water tanks and pipeline length. The object of study was evaluated for twelve Brazilian cities, according to Table 1, with representation for all the bioclimatic zones of NBR 15220-3 (ABNT, 2024). The cities chosen were based on the representative cities of NBR 15220-3 adapted to cover all five Brazilian regions. Thus, one city from the north, three from the northeast, three from the central west, two from the southeast and three from the south were included.

Table 1
Brazilian cities considered in the assessment of water heating systems

City	Bioclimatic zoning [A]	Average annual dry-bulb temperature (°C) [B]	Mean monthly minimum dry bulb temperature (°C) [B]	Mixed hot water temperature (°C) [C]
Curitiba	1M	17.4	14.6	40
Canela	1R	15.7	09.6	40
São Paulo	2M	19.6	16.8	40
Porto Alegre	2R	20.0	15.6	40
Vitória da Conquista	3A	20.6	18.1	38
Brasília	3B	21.1	19.5	40
Rio de Janeiro	4A	23.2	19.5	40
Goiânia	4B	23.2	20.5	40
Recife	5A	25.8	23.9	38
Cuiabá	5B	26.3	22.8	40
Fortaleza	6A	26.7	25.8	38
Palmas	6B	26.8	25.4	38

• Source: [A] NBR 15220-3 (ABNT, 2024) / [B] Available at INMETRO (2022) / [C] Annex B of Ordinance no. 309/2022 (INMETRO, 2022).

To calculate the energy required for water heating, it is essential to define the average number of inhabitants per household, set at four, and the amount of hot water consumed, set at 40 litres per inhabitant per day, as used by Santos and Giglio (2020). This amount of hot water differs from what is heated, as it is necessary to correct the volume through the thermal balance between hot and cold water to be mixed in the shower. Such detail was introduced in INI-R (INMETRO, 2022) and was used in all of the simulations and assessments considered in this study. Finally, the hot water pipeline was estimated at 25.0 metres in solar water heating systems and zero meters in electric showers, as water is heated at the point of use, i.e. in the shower.

Computer simulation of solar water heating

The computer simulation was carried out using the EnergyPlus computer programme based on the files and validated models by Santos and Giglio (2020), adapted to account for the twelve cities and variations in the latitude and tilt angle of the thermal collector. The inclination of the solar panels was set equal to the latitude so that the systems do not have very high tilt angles for cities in the south. This choice is in line with data from Santos and Giglio (2020). The other parameters are shown in Table 2. One chose to use parameters corresponding to the most conventional situations, such as using a thermal storage tank outside the building. Other details followed the INI-R guide and existing characteristics in social housing. The project was carried out using version 8.8 of the EnergyPlus programme.

Table 2
Simulation parameters using EnergyPlus

Variable	Description	Value or type
Simulation cities	Twelve cities are presented in Table 1	-
Climate file	ClimateOneBuilding EPW¹ files	TMYx 2007-2021
Location of the thermal storage tank	Considering options inside and outside the attic	Outside the attic
Thermal storage tank volume	Volume of hot water to be stored	200 litres
Daily mixed water consumption	The volume of mixed water consumption	160 litres
Collector orientation	Depends on the building’s roof orientation	North
Collector tilt angle	Tilt angle of the collectors	Equal to latitude in degrees
Collector surface area	The total area of the collectors	1.98 m²
Primary circuit insulation	Description of the insulation properties	No losses were considered in the distribution
Heating system backup	Type of backup energy to be used	Backup instantaneous water heater
Flow rate and duration	Characteristics of the shower	4 L/min per 40 minutes
Showering times	The hour that the shower is used	18:00 to 18:40h

• Source: adapted from Santos (2019).
• Note: ¹EPW stands for EnergyPlus Weather File.

Considering the collector azimuth deviation and tilt angle, the solar collector was modelled using SketchUp and the Euclid plugin. The solar collector surface model was imported into the EnergyPlus programme, version 8.8.0, to configure multiple objects representing a solar water heating system. These objects are interconnected through dependency relationships within the software. The main structure of the model is supported by loop configurations, in which two plant loops operate simultaneously and interact with the storage tank, as illustrated in Figure 2. The first step, the storage tank source loop, connects the collectors to the storage tank. In this part, cold water from the lower layers of the storage tank flows to the collectors, gains heat, and then returns to the storage tank, completing the cycle. This step includes all objects defining the collectors and the piping of the primary circuit.

The second cycle, the storage tank use loop, connects the tank to the water demand system. As hot water from the upper layers is drawn to meet demand, the storage tank is replenished with cold water. Several objects were configured for this part, including the properties of the pipes, a tempering valve to regulate water temperature by redirecting flow, an auxiliary support system, and hot water consumption equipment. An instantaneous water heater was utilised for the showerhead. All components – including collectors, storage tanks, piping circuits, tempering valves, and water-use equipment – are organised across thirteen branches, as shown in Figure 2.

Figure 2
Schematics of the solar water heating system primary structure model on EnergyPlus software

Sizing solar water heating using the labelling method

The INI-R methods, simplified and detailed, follow the equations described in INMETRO Ordinance 309 of 2022 (INMETRO, 2022), with the corrections described in technical note 01 (INMETRO, 2023). The parameters used in the detailed method are like those used in the simulation method, except for the climate file and showering times, as shown in Table 3. The main difference in input parameters is the showering time, which is non-specified in the INI-R methods. As such, thermal losses related to the hourly thermal dynamics from input solar energy and hot water use are not considered in INI-R methods, which may lead to more significant losses. For example, in colder climates such as Curitiba and Canela, between solar energy input during sunny hours and use by night, much heat has been lost to the atmosphere.

Table 3
Different simulation parameters using the detailed INI-R method

Variable	Description	Value or type
Climate files	Climate files differ from the ones in EnergyPlus	Data presented in Brazilian labelling
Showering times	Showering times are not specified in INI-R methods	-

• Note:
• ^¹
  data according to INI-R ordinance (INMETRO, 2022).
• The other parameters are the same as in Table 2.

Therefore, the main difference between the simulated model and the INI-R calculation method is the greater temporal granularity of the heat balance, with hourly heat exchange between the different water points. This approach provides greater precision in evaluating thermal losses and estimating shower times, understanding the need for electrical backup. INI-R methods do not consider sub-monthly thermal dynamics, which is better explored in EnergyPlus simulation in this study.

Another critical point is the difference in climate data relating to solar radiation. While the Energy Plus simulation uses data in EnergyPlus Weather Format (EPW), obtained from the Climate One Building database (Lawrie; Drury, 2024) in TMYx 2007-2021 format, the INI-R simulation was based on data from the Brazilian Solar Atlas (Pereira et al., 2017). This differentiation was made to agree on the most easily accessible data for each calculation method and use the suggested format by each model. Equations 1 to 17 show the INI-R detailed method (INMETRO, 2022). For more details on the connection between equations and the origin of the methodology, check INI-R (INMETRO, 2022). Python scripts were created to calculate all variables in the following equations.

The first equation of the INI-R method, Equation 1, shows the energy balance to heat water. Thus, it accounts for the energy required for heating minus the amount obtained from solar sources plus the energy lost. An efficiency factor is added to quantify the system’s type.

${\mathrm{C}}_{\mathrm{AAT}}=\frac{N_{ano}\cdot \left({\mathrm{E}}_{\mathrm{AA}}-{\mathrm{E}}_{\mathrm{AA},\mathrm{sol}}+{\mathrm{E}}_{\mathrm{per}}\right)}{{\mathrm{r}}_{aq,T}}$ (Eq. 1)

Where:

C_AAT is the thermal energy consumption for heating the building's water (kWh/year);

N_ano is the number of days of occupation per year; consider 365 days;

E_AA is the thermal energy required to meet the daily hot water demand (kWh/day);

E_{AA, sol} is heating water energy from solar sources (kWh/day);

E_per is the total thermal loss of the water heating system (kWh/day); and

r_aq,T is the coefficient of efficiency of the electrical backup water heater.

The second equation of the INI-R method, Equation 2, shows the energy required to heat water. Therefore, it depends on whether water is heated in storage or use. For solar water heating, one considers storage heating. Water's physical properties are also taken into account.

${\mathrm{E}}_{\mathrm{AA}}=\frac{\rho \cdot C_p\cdot V_{day }\cdot \left({\theta }_{A,arm}-{\theta }_{A,0}\right)}{3600}$ (Eq. 2)

Where:

ρ is the specific mass of water, equivalent to 1 kg/L;

C_p is the specific heat of water;

V_day is the daily volume of hot water consumption in the housing unit (m³/day);

θ_A,arm is the storage temperature of hot water (°C); and

θ_A,0 is the cold-water temperature (°C), assumed to be equal to the average monthly air temperature at the site.

With both equations established, one calculates the heat obtained from solar sources, excluding losses in the storage tank source loop. Equations 3 to 13 provide the INI-R detailed method for solar accounting, which considers climatic and operational parameters.

${\mathrm{E}}_{\mathrm{AA},\mathrm{sol}}=\frac{\underset{i=1}{\overset{12}{\Sigma }} \left(f_i\cdot E_{AA}\cdot N_i\right)}{365}$ (Eq. 3)

$f_i=1.029\cdot D_{1,i}-0.065\cdot D_{2,i}-0.245\cdot {\left(D_{1,i}\right)}^2+0.0018\cdot {\left(D_{2,i}\right)}^2+0.0215\cdot {\left(D_{1,i}\right)}^3$ (Eq. 4)

$D_{1,i}=\frac{E_{SAMês,i}}{E_{AA}\cdot N_i}$ (Eq. 5)

$D_{2,i}=\frac{E{P}_{Mês,i}}{E_{AA}\cdot N_i}$ (Eq. 6)

$E_{SAMês,i}=S_C\mid \cdot F_R^{\prime }\left(\tau \alpha \right)\cdot {\mathrm{EI}}_{mês,i}$ (Eq. 7)

$F_R^{\prime }\left(\tau \alpha \right)=F_R(\tau \alpha {)}_n\cdot \left[\frac{\left(\tau \alpha \right)}{(\tau \alpha {)}_n}\right]\cdot \frac{F_{\prime }^{\prime }R}{F_R}$ (Eq. 8)

${\mathrm{EI}}_{mês,i}=H_{dia}\cdot N_i$ (Eq. 9)

$E{P}_{Mês,i}=S_C\cdot {F^{\prime }}_R{U}_L\cdot \left(100-{\theta }_{amb,i}\right)\cdot \mathrm{\Delta t},\mathrm{i}\cdot K_1\cdot K_{2,i}$ (Eq. 10)

$F_R^{\prime }{U}_L=F_R{U}_L\cdot \frac{F_{\prime }^{\prime }R}{F_R}\cdot {10}^{-3}$ (Eq. 11)

$K_1={\left[\frac{V}{75\cdot S_C}\right]}^{-0.25}$ (Eq. 12)

$K_{2,i}=\frac{\left(11.6+1.18\cdot {\theta }_{A,uso}+3.86\cdot {\theta }_{A,0}-2.32\cdot {\theta }_{amb,i}\right)}{\left(100-{\theta }_{amb,i}\right)}$ (Eq. 13)

Where:

f_i is the monthly solar fraction;

N_i is the number of days in month “i”;

f_1,i is the parameter for month “i” calculated according to Equation 5;

EP_mês,i is the monthly solar energy not used by the collectors in month “i” (kWh/month);

D_2,i is the parameter for month “i” calculated according to Equation 6;

Δt is the number of hours in month “i”;

E_SAMês,i is the monthly solar energy absorbed by the collectors in month “i” (kWh/month); is the total absorption surface of the solar collectors (m²);

F'_R(τα) is a dimensionless factor, calculated using Equation 8;

EI_mês,i is the monthly solar irradiance incident on the collector surface in month “i” (kWh/(m².month));

F_R(τα)_n is the collector's optical efficiency factor, obtained from information provided by solar collector manufacturers (dimensionless);

$\left[\frac{\left(\tau \alpha \right)}{(\tau \alpha {)}_n}\right]$ is the angle of incidence modifier;

in the absence of this information, adopt 0.96 for collectors with a glass cover;

$\frac{F^{\prime }R}{F_R}$ is the correction factor of the collector/changer assembly; in the absence of this information, adopt 0.95;

H_dia is the solar irradiation incident on the inclined plane according to the orientation of the system (kWh/(m².day));

F'_RU_L is a correction factor, calculated using Equation 11 (kW/(m².K));

F_RU_L is the collector's overall loss coefficient, obtained from information provided by solar collector manufacturers (kW/(m².K));

K₁ is the correction factor for storage;

V is the volume of solar accumulation (litres) to guarantee the efficiency of the system;

K_2,i is the correction factor for the solar heating system;

θ_A,uso is the water use temperature (°C), as defined by INI-R; and

θ_amb,i is the average monthly air temperature of the site in month “i” (°C).

Lastly, losses in thermal reservoir and distribution systems are calculated. Thermal losses are considered depending on insulation parameters, as shown in Equations 14 to 17.

${\mathrm{E}}_{\mathrm{per} }={\mathrm{E}}_{\mathrm{per},\mathrm{tubo} }+{\mathrm{E}}_{\mathrm{per},\mathrm{res} }$ (Eq. 14)

${\mathrm{E}}_{\mathrm{per},\mathrm{tubo} }=\underset{l=1}{\overset{n}{\Sigma }} \frac{\left({\mathrm{H}}_{per\mathit{,}dist,l }{\mathrm{F}}_{per\mathit{,}tubo }\cdot {\mathrm{L}}_{tubo\mathit{,}l\mathit{ }}\cdot \left({\theta }_{\mathit{A}\mathit{,}\mathit{ }\mathit{u}\mathit{s}\mathit{o}\mathit{ }}-{\theta }_{\mathit{A}\mathit{,}\mathit{0}}\right)\right)}{1000}$ (Eq. 15)

${\mathrm{F}}_{\mathrm{per}, \mathrm{tubo} }=\frac{\pi }{\frac{1}{2\lambda }ln{\displaystyle }\frac{{\mathit{d}}_{\mathit{A}}}{{\mathit{d}}_{\mathit{R}}}+\frac{1}{{\alpha }_{\mathit{a}}\mathit{\cdot }{d}_{\mathit{A}}}}$ (Eq. 16)

${\mathrm{E}}_{\mathrm{per},\mathrm{res} }=\frac{\left({\mathrm{\theta }}_{A\mathit{,}arm}-{\mathrm{\theta }}_{A,0}\right)}{{\mathrm{\Delta \theta }}_{res\mathit{ }\mathit{,}sby}}\cdot {\mathrm{E}}_{res\mathit{ }\mathit{,}sby}$ (Eq. 17)

Where:

E_per,tubo is the heat loss in the piping of the hot water distribution system, without recirculation (kWh/day);

E_per,res is the heat loss of the hot water tank (kWh/day);

H_per,dist,l is the factor of hours of losses in the hot water distribution pipe (H_per,dist,l = 2.083 · V_day) (h/day), for the pipe “l”;

F_per,tub is the heat loss factor per meter of pipe for recirculation (W/(m.K)), according to Equation 16;

L_tubo,l is the length of the pipe “l” (m);

𝜆 is the thermal conductivity of the insulation (W/(m.K));

d_A is the external diameter of the insulated pipe, including insulation (m);

d_R is the pipe diameter (m);

α_a is the heat transfer coefficient (W/(m².K));

Δθ_res,sby is the average temperature difference in tests with the tank on standby (°C) and 29 °C should be adopted; and

E_res,sby is the specific standby heat loss of the tank (kWh/day).

Technical note 01 (INMETRO, 2023) added the simplified INI-R method to facilitate the evaluation. It should be reiterated that the aim of both INI-R methods is not necessarily to obtain exact electricity consumption but to estimate the reduction by adopting solar heating. Equations 18 and 19 (INMETRO, 2022), included in the simplified method, estimate the solar energy available through solar heating. Using the value of 'Eaa,sol', the calculation is carried out similarly to the detailed method.

$E_{AA,sol}=P_{ES,day}\cdot \left(\frac{H_{year}}{5.35}\right)$ (Eq. 18)

$P_{ES, day }=\frac{\left(P_{ME, month }\cdot N_C\cdot S_C\right)}{30.42}$ (Eq. 18)

Where:

E_AA,sol is the energy available from solar water heating (kWh/day);

P_ES,day is the average daily specific energy production of the system (kWh/day);

H_year is the solar irradiation on the inclined plane (kWh/(m².day));

P_ME,month is the average monthly energy production of the collector by area (kWh/(month.m²));

S_C is the collector area (m²); and

N_C is the number of collectors (units).

Electricity consumption due to electric showers

Heating by electric showers was sized using a Python algorithm that evaluates monthly consumption. The method considers that when switched on, the shower consumes between the minimum and maximum values established in the Institute for Technological Research (IPT) energy efficiency table (IPT, 2023) in a linear correlation. Figure 3 shows the parameters used for calculating the electricity consumption of electric showers.

Figure 3
Electric shower simulation parameters

The hot water temperature was taken as 30, 35 and 38°C to understand the impact of this definition on electric shower consumption. The electric shower power was defined based on the work of Sangoi and Ghisi (2019), determined using Equation 20 (Sangoi; Ghisi, 2019) and adapted for each local monthly minimum temperature. The nominal power was determined according to the existence of an electric shower on the market, with the lowest power immediately above the calculated value. Finally, Equation 21 (Sangoi; Ghisi, 2019) shows the consumption if the electric shower is switched on at nominal power for the entire shower period.

$P_{nom}\ge \frac{q\cdot c\cdot \left(T_{use}-T_{min}\right)}{860\times n}$ (Eq. 20)

$C_{CE}=P_n\cdot h\cdot H_{year}$ (Eq. 21)

Where:

P_nom is the nominal power of the shower (kW);

q is the flow rate of the shower (l/h);

c is the specific heat of water (1.00 cal/g°C);

T_use is the water consumption temperature, according to INI-R (°C);

T_min is the average monthly minimum temperature (°C); and

h is the number of hours the electric shower operates per day;

H_year is the number of days in the year.

The nominal power was determined according to market models.

Comparison with the literature and economic assessment

The comparison was made to understand the INI-R methodology and how it conforms to the thermal balance. The aim of the INI-R is not to obtain the exact energy consumption but rather to estimate the electricity reduction using a method with fewer variables than the simulation and the actual parameters that influence the thermal balance. Therefore, we want to understand the difference between the simulated model and the detailed and simplified INI-R methods.

Kendall's correlation was used to compare the solar water heating results, the climatic variables, and the latitude. Kendall's correlation was chosen due to the low number of cases (cities) evaluated in the study. The following variables were included in the correlation analysis: Average Annual Dry Bulb Temperature (AADBT), Average Annual Daily Solar Radiation (AADSR) and Average Annual Thermal Amplitude (AATA). The goal was to understand whether the three climatic variables are correlated with energy consumption estimates using the three assessment methods. Also, the latitude of each city was included.

Finally, an economic analysis of the systems was carried out to understand the cost of operation over the system's useful life. To this end, a useful life of 20 years was assumed, in line with other studies in the literature (ABNT, 2021; Antunes; Ghisi; Severis, 2020; Vaz; Ghisi; Thives, 2020). A minimum attractiveness rate (MAR) of 10% per year is also assumed to align with the basic Brazilian interest rate (SELIC). With these definitions, the economic feasibility analysis can be carried out using Equations 22 to 24. The equations were implemented in Python, considering all variables presented, and calculated for each location assessed.

$\mathrm{Im}áx=\underset{t=0}{\overset{n}{\Sigma }} \frac{E_{t,j}}{(1+MAR{)}^t}$ (Eq. 22)

$E_{t,j}={\mathrm{fare}}_{t,j}\cdot \left({\mathrm{C}}_{EL{E}_j}-{\mathrm{C}}_{SO{L}_j}\right)\cdot \frac{1}{(1+k{)}^t}$ (Eq. 23)

$fare{ }_{t,j}=fare{ }_{base \mathrm{\_}j}\cdot {\left(1+ fare\_adj{ }_{-}\right)}^t$ (Eq. 24)

Where:

Imáx is the maximum investment possible in which the solar water heating is feasible (R$);

E_t,local is the adjusted annual savings through the use of solar heating for year t and location j;

n is the number of years of analysis, equal to 20 years in the study; t is the assessed year; j is the assessed city, fare,_t,j is the fare for year t for city j;

C_{ELE_j} is the annual electricity consumption for the house in city j considering only the electric shower;

C_{SOL_j} is the annual electricity consumption for the house in city j considering solar heating and electric backup;

k is inflation, considered to be 4% per year;

fare_{base_j} is the base fare in the year 2024 for each city j; and

fare_adj is the annual fare adjustment factor, set at 6% per year.

The maximum investment possible was the variable assumed to understand the maximum cost of the system that maintains viability; in other words, that generates a Net Present Value (NPV) equal to zero. For the variable costs, only the cost of electricity is assumed, with a fare according to the city selected. The fares vary between 0.57 and 0.88 R$/kWh in Brazil, with the minimum value being for Curitiba and the maximum for Cuiabá. Intranational variation in economic viability is based on the different electricity fares.

Results and discussions

The energy consumption for the different systems is shown in the following subsections. According to the Household Survey (Eletrobrás, 2019), 40.90% of the country's households heat water using electricity, 0.96% use solar energy, and 56.99% have no water heating. Thus, the assessment compared the electrical alternative, mainly the electric shower system, and the solar water heating. Discussions on the necessity of water heating were also provided.

Solar water heating and electric showers

Figure 4 shows the results for the twelve cities and the three sizing methods. In all scenarios, energy consumption increases as the latitude increases towards the south. Eight cities show zero or almost zero consumption, using both INI-R methods (simplified or detailed), indicating that solar energy is sufficient to meet all hot water demands. This solar heating sufficiency ratio is also seen in the simulation, albeit at a lower value. For the simulations using EnergyPlus, all the cities presented some energy consumption, albeit low, related to continuous cloudy days when the solar energy obtained is low. In these cases, according to INI-R, electrical backup is required to supply the mixed water temperature of 38 and 40°C.

Figure 4
Energy consumption for backup solar water heating

Another point of interest is the difference between the detailed and simplified INI-R methods, which show that using the simplified method results in lower consumption for the cities evaluated. Regarding suggestions for the INMETRO Ordinance, the possibility of using both methods will lead to using only the simplified method since it is less laborious and has more favourable results for certification. However, in some cities, the energy consumption of the backup obtained by the simulation method was high, in contrast to the zero consumption by the simplified method. In other words, hourly variations in the energy flow for heating and system operation issues must be accounted for in the quantification but are impossible with the simplified method.

Finally, Figure 5 shows the correlation matrix between the variables considered. The simplified method does not consider the local temperature when estimating the solar energy obtained; it is only used to calculate the heating energy required. As a result, the simplified method showed a lower correlation with TBSMA (-0.68), unlike the detailed (-0.88) and simulated (-0.81) methods.

Figure 5
Kendall correlation between the different variables assessed

The climatic variables (AADBT, AADSR and AATA) correlate more significantly with the energy consumption obtained from the computer simulations and detailed INI-R methods. For example, solar energy gain is related to AADBT and AADSR through the thermal exchange of water in the collectors and the ambient temperature. It is, therefore, understandable that methods that present this thermal exchange in more detail are more accurate. The simplified method does not include AADBT, generating less correlated results. On the other hand, thermal amplitude was not highly correlated with energy consumption. However, it should be noted that only some scenarios were modelled in the study.

Figure 6 shows the result of the Python simulation of the different energy consumption for electric showers in the country. The definition of the temperature of use and use without heating impacts electricity consumption. For example, using the shower at full power all year round generates a consumption of between 1070 and 1825 kWh/year. Using showers with adjustable power and a shower temperature of 30°C consumes between 764 and 1030 kWh/year. The use of electric showers switched off in months when the outside temperature is above 26°C results in consumption between 143 and 374 kWh for Fortaleza, Palmas, Recife and Cuiabá.

Figure 6
Annual simulated energy consumption of electric showers

Understanding usage patterns is necessary to compare water heating systems correctly. Simply comparing systems with and without solar heating without understanding user details can overestimate the consumption of electric showers and underestimate the consumption of solar heating systems. The solar heating system can be optimised to reduce, or even zero, energy consumption. Therefore, it is understandable that the INMETRO programme label values this technology over the electric shower.

Feasibility of solar water heating

The results obtained in this study corroborate the results of Vaz et al. (2023), who indicated that solar water heating systems are the most energy-efficient. In any case, the interest in incorporating user comfort into water heating analyses is reiterated, with a better understanding of temperature and shower flow dynamics. One hopes to obtain more specific results for different regions and understand the energy impact of this system in the country.

Table 4 summarises the data used and the results obtained in the maximum investment analysis. A first interesting observation is the difference in electricity fares across the country, with lower electricity fares in Paraíba, Santa Catarina and Paraná. Many factors drive the electricity fare, making it variable across the country. Therefore, it does not follow latitude or geographical conditions, which means that higher electricity reduction may not lead directly to better feasibility. Cities with smaller reductions in electricity bills may obtain better economic results due to higher fares and, consequently, a higher economy by adopting solar water heating.

Table 4
Summary of results of the economic feasibility analysis

City [3]	Annual electricity consumption with electric shower (kWh/year)	Annual electricity consumption with solar water heating (kWh/year) [1]	Annual electricity reduction by adopting solar water heating [2]		Base electricity fare for residential buildings (R$/kWh)	Maximum investment in which the system is feasible (R$)
			Absolute values (kWh/year)	Relative reduction (%)
Curitiba	1496	408	1088	-73	0.629	8,967.37
Canela	1594	331	1263	-79	0.701	11,599.62
São Paulo	1371	240	1131	-82	0.671	9,947.66
Porto Alegre	1347	273	1074	-80	0.701	9,864.69
Vitória da Conquista	1263	172	1091	-86	0.821	11,742.60
Brasília	1332	66	1266	-95	0.743	12,321.67
Rio de Janeiro	1239	156	1083	-87	0.870	12,344.48
Goiânia	1235	30	1205	-98	0.745	11,768.11
Recife	850	56	794	-93	0.744	7,738.82
Cuiabá	1029	33	996	-97	0.847	11,056.85
Fortaleza	816	11	805	-99	0.722	7,619.25
Palmas	816	17	799	-98	0.823	8,616.95

• Note:
• ^[1]
  Annual electricity consumption for solar water heating was obtained via the simulation method.
• ^[2]
  Difference between consumption with electric showers and solar water heating.

It is essential to highlight that during this research, the Pronasol policy (Brazil, 2023) was not considered because it is still under discussion in Brazil. Therefore, one expects that if approved, such legislation will improve the feasibility of the systems analysed herein and bring higher financial benefits to the final user. For example, solar water heating may be feasible considering the expected electricity consumption of electric showers and solar water heating via simulation, Python script, and EnergyPlus. However, it is essential to carefully consider the climatic parameters, the electricity fare of the city, and the operation characteristics, which may hinder or facilitate the feasibility.

Future studies and limitations

It is necessary to note that several limitations are included in the study and should be better evaluated in future studies. Firstly, the IPT data used to define the electricity consumption of the electric shower was obtained from past reports (2023) and may not reflect new technologies used for electric showers. The simulation is also based on linear correlations of electricity consumption between the minimum and maximum values defined by IPT, and it may differ from the curve between temperature increase and appliance consumption in existing appliances. In addition, the electric shower was considered to be always on, with a fixed temperature increase to predetermined values. This definition may differ from reality, in which users have specific habits according to their culture. It should also be noted that the flow rate was not evaluated in this study, and the minimum flow rate of the appliances and showers for a fixed period was considered, which may differ from the reality of Brazilian residential units.

Another limitation refers to the cities selected, in which future studies can consider more cities in the assessment. In that case, it might be easier to observe regional trends in feasibility optimisation and electricity consumption, which could be of interest for decision-making in public policies on water heating. This factor can be added to a more robust analysis of electricity tariffs in Brazil and the comparison between tariffs for social housing and standard residential typologies, which should modify the financial assessment results. Also, the cost of installation and the system of solar water heating may vary among the cities, which was not included in our study and may be considered in future assessments. In other words, ensure that the systems benefit both the user and the utility company so that the relevance of tariffs, location, climatic conditions and public policies in solar water heating is understood.

Another point that could be studied in future research is climate change's impact on Brazil's water-heating economy. With warmer cities, one may see a decrease in water heating use or greater use of showers in summer mode with less temperature increase. Also, possible changes in radiation patterns can impact in the thermodynamics of solar water heating. As a result, solar water heating systems may be less efficient in reducing electricity consumption and a consequent reduction in the savings from using the technology. Such studies can help with decision-making in long-term sectoral policies, with the example of Pronasol.

Besides the variability of the climate conditions, design parameters are also influential in the results. For instance, if other tilt angles are used, one should observe changes in electricity consumption and therefore in savings. The recommendation of using the latitude plus ten degrees was not considered, as this value would imply very steep installations in the southern cities, and we opted to use only the latitude as a reference for the tilt angle. Also, efficiencies may vary among the possible technologies for the collector’s surface, which would also impact on the results. Future studies should address the sensitivity of the parameters presented in this study, including the INI-R and simulation methods. Also, studies may also use the solar fraction as a key performance indicator, focusing on the system’s thermal balances.

Finally, the simulation models in EnergyPlus can be adapted to understand other system variations in solar water heating effectiveness. Changes in the orientation or tilt angle of the solar panels, the technology used in the solar energy collection surface, shower times, shower flow rates, types of pipes and other system operation and construction variables all impact the results and should be evaluated in greater detail. In general, there has been little study of solar water heating in Brazil, and it is a technology that has been and is being widely recommended because it is considered sustainable. Still, it must be evaluated to understand the intranational variations that impact the sustainability of using solar water heating.

Conclusions

This study aimed to compare water heating systems and the methods used to evaluate their electricity consumption in Brazil. The use of thermal and energy simulation, as well as details of electric shower consumption, is significant for understanding the impact of this system on buildings and national energy consumption. The main result is the differences between energy assessment methods for water heating. The choice of assessment method has an impact on the scale of consumption reduction through solar heating and the energy optimisation of this system, contributing to an over- or under-quantification of electricity consumption that can mistakenly influence decision-making regarding the use of solar heating. For example, the possibility of using both methods of normative instruction will eventually lead to using only the simplified method, which is less laborious and has more favourable results for the label. It is also essential to analyse the impact in each city, as there are significant differences in energy consumption between cities in the south and north/northeast of the country.

We highlight that these findings should be explored in the future to provide better information for water heating assessment methods in the country, such as creating new tools based on the existing procedures. Such tools may include more variables such as the use patterns, and consider an hourly timestep rather than a monthly basis, as the current INI-R methods use. Also, the simulation procedures for electric showers should be better explored, as water heating is one of the main contributors to electricity consumption in some of the bioclimatic zones. Thus, there is a need for an update on the water heating evaluation methods in the country, as there is still a lack of depth in the thermal dynamics and energy assessments.

Regarding the results of the systems, there are more significant electricity savings in absolute values, kWh/year of reduction, in Brasília, Canela and Goiânia. Although solar systems cannot reduce electricity consumption to zero, solar energy can significantly reduce the electricity demand. This is the case in Canela, which has cold nights and heavy use of the electric backup system. Brasília and Goiânia, on the other hand, consume less electricity in electric showers because they are warmer cities. However, solar heating systems can reduce practically zero electricity consumption, with backup systems rarely used. It can, therefore, be seen that there are interactions between climatic variables, the pattern of hot water use, and the system's efficiency, which can be explored better in future studies.

Finally, the electricity reduction in different cities is also compounded by the variability of electricity tariffs in Brazil, which reflects which cities have shown the system to be economically viable. Cuiabá, for example, obtained less than 1000 kWh/year in electricity consumption reduction with the solar water heating systems due to its general low heating demand. However, due to the high local electricity tariff (0.847 R$/kWh), it was economically viable for solar water heating systems costing 10,000 Brazilian reais. It is worth reiterating that the study could be applied to more cities in the future so that it can provide more data on the economic viability of solar water heating in Brazil and be able to encourage, along with the new public policies that are under discussion, the sustainable use of electricity for water heating.

Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.

Data availability

Data will be made available upon reasonable request. Existing files that can be provided include the IDF files used in the study and the INI-R Python codes, which include detailed and simplified methods.

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Edited by

• Editora de seção:
  Luciani Somensi Lorenzi

Publication Dates

• Publication in this collection
  02 June 2025
• Date of issue
  2025

History

• Received
  30 Jan 2025
• Accepted
  07 Apr 2025