Energy Consumption In Residential Buildings Research Articles

Residential building energy consumption estimation: A novel ensemble and hybrid machine learning approach

In recent decades, there has been a substantial rise in both worldwide energy consumption and the accompanying increase in Carbon Dioxide (CO2) emissions, primarily propelled by population growth and the escalating demand for personal comfort. Operational energy consumption in buildings constitutes about 30% of the world's total final energy usage, underscoring the significance of predicting building energy usage for effective energy planning, management, and conservation. This study has enhanced the prediction of heating load (HL) and cooling loads (CL) for residential buildings using novel and dependable machine learning (ML) techniques. The study utilized two base models: Adaptive Boosting (ADA) and Extreme Gradient Boosting (XGBoost). An ensemble consisting of ADA and XGB was constructed to improve the model's performance, aligning with the principles of the Dempster-Shafer theory. To optimize the efficiency of ADA and XGB, five innovative optimizers, namely Victoria Amazonica Optimization (VAO), Giant Trevally Optimizer (GTO), Covariance Matrix Adaptation Evolution Strategy (CMAES), Coyote Optimization Algorithm (COA), and Mountain Gazelle Optimizer (MGO), were integrated. Statistical analysis has been employed to evaluate the performance of the proposed models. The results highlight the effectiveness of CMAES in optimizing the XGB and ADA models for predicting HL and CL. The most accurate result was achieved by the XGCM hybrid model, as evidenced by the impressive total R2 value of 0.9934 in HL and 0.9911 in CL prediction. The experimental findings illustrate that the suggested approach exhibits superior predictive performance across various scenarios of building energy consumption.

Predicting thermal comfort of residential shower rooms for optimal showering energy use

Showering is a typical daily activity directly related to people's comfort and health, dominating thermal energy consumption in residential buildings. This study developed a mathematical model to predict people's mean skin temperature (MST) and dynamic thermal sensation (DTS) during undressing and standing showering periods to identify the comfortable showering environment for different occupants. The model's validity was supported by comparing the calculated MSTs with the results collected by a previous experimental study. Moreover, 31 subjects (of different sexes, weights, and heights) were involved in the case studies. Results of the case studies showed that female and underweight subjects' MST and DTS varied more significantly than male and overweight subjects during undressing and showering periods. Additionally, it was found that both water and air temperatures significantly impact occupants' MST and DTS during showering, and the impact of water temperature was much more significant than the impact of air temperature. Moreover, by combining a previously developed energy consumption model, an optimal showering environment can be identified where both maintaining comfort and saving energy can be achieved. Based on the simulation results, the water temperature during showering was suggested to be not higher than 40 °C, especially for longer showering, to avoid thermal discomfort and energy overconsumption. However, the model was established based on theoretical deduction, with assumptions made. Additional measurements were needed to validate the model further and improve it.

Energy and economic analysis of building integrated photovoltaic thermal system: Seasonal dynamic modeling assisted with machine learning-aided method and multi-objective genetic optimization

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications.

Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II

The energy consumption, daylighting, and thermal comfort of buildings directly affect the three key goals of residents. However, there is little research on the optimization of energy consumption, daylighting, and thermal comfort in residential buildings in China. Therefore, this study proposes an optimization framework that combines Bayesian optimization with extreme gradient boosting trees (BO-XGBoost) and non-dominated genetic algorithm-II (NSGA-II) to study the multi-objective optimization of residential building performance. This paper first uses Grasshopper to simulate and obtain a dataset through Latin hypercube sampling (LHS). BO-XGBoost is used to establish the regression relationship between building envelope design parameters and residential building performance. Then, the obtained regression model is used as the fitness function of NSGA-II to get the Pareto optimal solution set. Finally, the ideal point method is used to obtain the optimal combination of building envelope structure parameters for residential buildings. Taking a residential building in a hot summer and cold winter area as an example, the effectiveness of this method is verified. The results show that (1) BO-XGBoost has excellent predictive performance, with R2 values of 0.997, 0.960, and 0.994 for energy consumption, thermal comfort, and daylighting, respectively. (2) The proposed BO-XGBoost-NSGA-II can effectively achieve multi-objective optimization. Compared with the initial scheme of the case building, energy consumption is reduced by 44.1%, thermal comfort index is reduced by 10.9%, and daylighting performance is improved by 1.7%. Therefore, the proposed method can effectively optimize the performance goals of residential buildings and provide practical ideas for similar problems.

Assessing the Energy Performance and Retrofit Potential of the 1980–1990s’ Residential Building Stock in China’s Jiangsu Province: A Simulation-Based Study

The building operation sector in China represents 22% of the national energy consumption and 22% of the carbon emission, of which urban residential buildings accounted for 24% in 2019. Such figures for the housing sector are projected to increase sharply in the near future, while China aims to peak CO2 emissions by 2030 and reach neutrality before 2060. To reduce the impacts of the urban housing sector and address the energy use and waste generated by large-scale demolition and reconstruction, the central government started promoting the energy retrofit of urban residential buildings, raising such policies to the national strategic level. Jiangsu Province is one of the most urbanised, with a rapid growth in the energy consumption of residential buildings. The Multi-Danyuan and Single-Danyuan Apartment built in 1980–1999 are the most representative residential types in its urban areas. While still adequate functionally, they were designed and built to low energy standards and show significant potential for energy retrofit. Nonetheless, their current performance and energy-saving potential are under-researched, while more detailed and reliable data would be critical to support retrofit design and policy making. This study investigates and characterises the typical use and energy performance of the two building types. Additionally, seven measures and six retrofit scenarios were identified based on the optimal energy reductions and regulations from selected countries. The simulations indicate that, without intervention, the energy consumption of the typical urban residential buildings can reach 122 kWh/m2 under the typical high-energy user scenario. By selecting a set of effective energy-saving measures, the operational energy use for heating and cooling can be reduced by up to 52.4%. Current local standards prove cost-efficient, although less effective in reducing energy use compared to international best practices, indicating potential improvements to the contribution of building retrofit towards achieving the national carbon reduction goals.

Photovoltaic technology in rural residential buildings in China: a comprehensive review on applications, development and its benefits

ABSTRACT China’s rural residential photovoltaic system has been greatly developed in recent years. However, most existing researches, are difficult to reflect the real development situation of the whole system. This study systematically sorts out the results in related fields through the literature review, aiming to clarify the current situation of applications and benefits of photovoltaic technology. The results show that currently the photovoltaic power generation technology is relatively mature and widely applied, and passive photovoltaic technology can play a greater role in reducing energy consumption in rural residential buildings in China. However, the development of photothermal and photovoltaic combined with other energy technologies, as well as the integration between photovoltaic technology and buildings are relatively lagging behind. Besides, the life cycle analysis and evaluation of photovoltaic system has not been established. In view of opportunities and challenges, this study prospects two future development directions. Firstly, the system coupling of photovoltaic technology should be strengthened, especially between the joint supply subsystems, between the active and passive technologies, and between photovoltaic technology and buildings. Secondly, the rationality of photovoltaic technology evaluation should be ensured, particularly to cover the three aspects of economy, ecology and society on the whole life cycle dimension.

In Situ Performance Analysis of Hybrid Fuel Heating System in a Net-Zero Ready House

The global population’s growth and increased energy consumption have driven greenhouse gas (GHG) emissions. In Canada, the residential sector accounts for 17% of secondary energy use and 13% of GHG emissions. To mitigate GHG emissions, promoting renewable energy and efficient heating systems is crucial, especially in cold climates like Canada, where there is a heavy dependency on fossil fuels for space heating applications. A viable solution is hybrid fuel heating systems that combine electric-driven air-source heat pumps (ASHPs) with natural gas tankless water heaters (TWHs). This system can alternate its operation between the ASHP and TWH based on efficiency and real-time energy costs, reducing grid peak demand and enhancing resilience during power outages. Although lab experiments have shown its benefits, in situ performance lacks evaluation. This study analyzes the in situ energy performance of a net-zero ready house and its hybrid fuel heating system, assessing energy consumption, hourly space heating output, and system heating performance. HOT2000 is a robust simulation software designed for assessing energy consumption, space heating, cooling, and domestic hot water systems in residential buildings. An artificial neural network model was developed to predict the energy performance of the hybrid fuel system, which was used as a substitute for monitored data for evaluating the HOT2000’s simulation results under the same weather conditions. Therefore, this study proposes a comprehensive framework for the in situ performance analysis of hybrid fuel heating systems. This study then, using HOT2000 energy consumption results, evaluates the life cycle costs of the hybrid fuel system against conventional heating systems. Furthermore, this study proposes an economical control strategy using in situ data or manufacturer specifications.

Study on program methods for energy monitoring of buildings

Energy monitoring is one of the effective measures aimed at reducing and maintaining energy consumption of residential and public buildings at the proper level, taking into account climatic factors. This article provides information about the process of monitoring the energy consumption of a building. A brief analysis of methods for monitoring the energy consumption of different types of buildings has been conducted. In particular, the methods of "Control and Normalization" based on the Energy-Temperature diagram based on the operating mode of the building are considered. In addition to the methods, information is provided on the information system "energy monitoring of buildings" developed on their basis. Also presented the process of implementing energy monitoring using the developed information system. The use of energy monitoring makes it possible to determine overconsumption in a timely manner and due to this to optimize the current level, as well as to determine the target values of energy consumption in buildings.

Thermal Performance Evaluation of TIM Combined with Residential Windows in Different Climatic Regions in Iran

Windows play a significant role in the increase and loss of heat from the building envelope and determine the quantity, quality, and distribution of daylight. A strategy that involves incorporating transparent insulating materials into a double-glazed window offers the potential to provide combined improvements in thermal and daylighting performance. The thermal properties of transparent insulation materials in windows depend on various factors, such as the type of insulation material, thickness, geometry and insulation structure, location, and orientation of the window, among others. The aim of this research is to optimize three criteria: "thickness," "location of transparent insulation relative to window layers," and "direction of the wall with transparent insulation of the building window." The goal is to minimize thermal loads and reduce energy consumption in residential buildings. To achieve this, a real model was selected, and Design Builder software was used to measure the "heating load," "cooling load," and the sum of these two loads as the "total thermal load" for all three criteria in three cities of Iran with different climates: Tehran (moderate climate), Ahvaz (warm climate), and Tabriz (cold climate). The results of the research showed that for the city of Tehran, 3-inch insulation in the middle of the double-glazed window and the south front is optimal. For the city of Tabriz, 5-inch insulation on the inner surface of the window and the western front is optimal. And for the city of Ahvaz, 3-inch insulation on the outer surface of the window and the eastern front is optimal. It is worth noting that the annual heating load and total annual heating load for all three criteria have the highest values in Tabriz city. Therefore, it is recommended to use HSNPS insulation in transparent windows to reduce energy consumption in Tabriz (cold climate).

The Impact of Residents' Behaviour on the Energy Consumption of Buildings: A Case study of Residential Building in Guelma, Algeria

Since the beginning of this century, energy consumption has grown rapidly in various regions around the world. It seems that energy consumption will continue to increase, partly due to economic growth and partly due to an increase in per capita energy consumption. Since the implementation of the first energy-saving measures, the issue of energy efficiency in buildings has always been at the heart of energy and climate policies, whether at the European, national or local level. In this context, this article is organized into parts, starting with an introduction that summarizes the situation and then the methodology that attempts to examine the energy efficiency of buildings by taking into account the synergy between urban planning and occupant behaviour through a questionnaire. So, the question that arises is how can energy consumption in residential buildings be influenced by occupant behaviour. Among the constraints that we found in this work were the gaps in the information and the subjectivity of the answers obtained by the occupants. The main results show a significant impact of occupant behavior on energy consumption. After a questionnaire on 20 dwellings, we obtained answers on the equipment used in all the dwellings. Then, we made a provisional calculation of the consumption according to the days and times that the occupants were present in their homes. Finally, energy efficiency offers governments and communities a cost-effective way to achieve a variety of goals, such as reduced energy consumption, lower emissions, financial savings, energy security, and positive health impacts.

Implication of Shading Passive Strategies in Buildings of Hot and Humid Climates for Energy Optimization: Lessons from Vernacular Dwellings in Nigeria

Residential buildings consume approximately 30% to 40% of total primary energy, representing a significant share of the world's energy consumption. The contemporary designs of residential high-rise buildings in Nigeria borrow from different climates and adapt to Nigeria's hot and humid climate. Shading is an effective strategy for reducing solar heat gain and improving cooling conditions in hot and humid climates. This study tries to explore the most effective and most adaptable shading elements in the study area to be implicit in the contemporary design of high-rise residences. This study has a particular emphasis on shading techniques; hence, it will examine and present existing literature on shading devices and strategies. The aim of this research is to minimize energy consumption in residential buildings in Nigeria. Therefore, the research investigates the most effective shading strategies in the vernacular buildings that can be utilized as design criteria in future buildings. The findings led to identifying the effective and adaptable shading strategies found in vernacular architecture, which can be utilized in contemporary high-rise residential buildings in Nigeria for energy optimization. Finally, the research suggests guidelines to support architects in reducing energy usage in high-rise residential buildings in Nigeria.

An energy-saving design method for residential building group based on convolutional neural network

Current building energy saving methods mainly focus on the individual building, seldom focus on the building group. Different from the energy saving design method which takes the individual building as the object, the energy saving design of building group also needs to consider the influence of microclimate on building energy consumption, because microclimate is the boundary condition for building energy consumption calculation. The purpose of this study is to investigate the influence of the building group form and geometric factors on the energy consumption of residential buildings in Beijing. First, this study developed a complex plane generator based on convolutional neural network (CNN), and used the generator to extract and classify the plane of residential buildings in Beijing, and generate three standard residential building models. Secondly, the microclimate conditions of the three standard models in summer and winter were calculated by ENVI-met, and the energy consumption of individual building with different geometric form was calculated by DesignBuilder. Based on the above simulation results, the influence of group forms and geometric factors on microclimate and the influence of geometric form on energy consumption of individual building were quantified, and the influence law of group forms and geometric factors on energy consumption of buildings was revealed. The obtained law can be used to quickly estimate the change of total building energy consumption when the group form or geometric factor changes at the initial stage of the design of residential buildings.

Prediction of an Efficient Energy-Consumption Model for Existing Residential Buildings in Lebanon Using an Artificial Neural Network as a Digital Twin in the Era of Climate Change

Environmental factors, such as climate change, have serious consequences for existing buildings, including increased resource consumption and footprint, adverse health effects, and reduced comfort for the occupants. To promote sustainability and address climate change, architecture must embrace digitalization. Buildings can be built digitally, analyzed in real time, optimized for energy consumption, and utilized to reduce carbon emissions and achieve zero energy consumption using digital twin technology. Currently, Lebanon’s residents are turning to solar power to generate renewable energy as a result of a lack of energy supplied by the government. In this study, a digital twin model was designed using an artificial neural network (ANN) to investigate the energy consumption of residential buildings. The main idea was to assist architects and engineers in forecasting energy consumption for different design materials by selecting the most effective alternate design for materials with building envelope characteristics, such as exterior walls, roof insulation, and windows, to minimize the consumption of energy in a residential building, hence resulting in a green building. The data simulations used in the digital twin model were carried out using Quick Energy Simulation Tool (eQuest) software; 1540 simulation results were used for different thicknesses of insulation material, values of conductivity, and window types. The digital twins were designed using an artificial neural network model. The results of the investigation and the accompanying eQuest output results were found to be precise and very similar.

Air purifier or fresh air system? A comparison of energy consumption and cost in urban China residential buildings

Air purifier or fresh air system? A comparison of energy consumption and cost in urban China residential buildings

A comprehensive review and sensitivity analysis of the factors affecting the performance of buildings equipped with Variable Refrigerant Flow system in Middle East climates

Variable Refrigerant Flow (VRF) systems are becoming increasingly popular in commercial and residential buildings due to their flexibility and efficiency. Building design and operational parameters play an important role in the process of predicting cooling loads, and these variables contain a variety of uncertainties that must be taken into account when seeking to obtain a reliable prediction. It is common practice to oversize the Heating, Ventilation, and Air Conditioning (HVAC) system installed in a building in order to reduce uncertainties in its performance. However, oversizing is undesirable due to additional capital and operating costs, inefficient space utilization, and excessive emissions of greenhouse gases. This study provided a comprehensive literature review on previous uncertainly analysis studies from six perspectives: classification of uncertainty factors, sensitivity analysis methods, building energy model for uncertainty analysis, sampling method, the most effective parameter, and distribution of uncertainty parameters., and then developed a framework to investigate the effects of uncertainty of operational and design parameters on annual cooling load, total energy consumption, HVAC electricity use per conditioned floor area, VRF system cost, and the cooling-to-total electricity consumption ratio (R) in residential buildings equipped with VRF systems located in the Middle East (ME) region (Kuwait City, Tehran, and Istanbul). Artificial Neural Network (ANN) model was also developed to predict output parameters in residential building. Latin Hypercube Sampling (LHS) simulation was also applied to generate near-random samples of uncertainty parameter values from a multidimensional distribution. It should be mentioned that a correlation was derived in this study to predict VRF cost. The study evaluated thirteen uncertainty parameters related to building characteristics, occupants, building energy systems, VRF variables, and lighting conditions. Several methods were used to evaluate the effects of input uncertainty parameters on the output variables, namely, the Standardized Regression Coefficient (SRC), Partial Correlation Coefficient (PCC), and Spearman Rank Correlation Coefficient (SRCC) or Pearson Product-Moment Correlation Coefficient (PPMCC). The results indicated that VRF cost and HVAC electricity use per area have a greater coefficient of variation compared with other output parameters. The density distribution of output parameters indicated that uncertainty parameters had the greatest impact on output parameters in Kuwait (hyper arid climate), whereas in Istanbul (humid subtropical climate), they were the least. Among the variables examined in the Sensitivity Analysis (SA), cooling setpoint had the greatest impact on residential building energy consumption in ME climates. Finally, this paper highlights emerging trends and offers recommendations for advancing future research in the realm of building energy uncertainty analysis.

Occupant behaviour and the potential of automating lighting control in terms of energy consumption – is there a link for residential buildings?

The implementation of occupancy-controlled and daylighting-dimmed lighting systems has an impact on the energy consumption of residential buildings. The BAC factor method of EN ISO 52120-1 estimates that 8% of the lighting energy can be saved compared to conventional manual control. However, it is assumed that their ability to potentially lower the lighting energy consumption is strongly related to external factors, such as the extent of daylight entrance and the behaviour of the inhabitants. By means of simulations in EnergyPlus, the performances of automated and manual lighting control are compared for an apartment and semi-detached building located in Brussels (Belgium) with variation in the occupant behaviour and orientation.It appears that an automated lighting control including 0-100% dimmer reduces the lighting energy demand for all investigated cases with savings up to 38.4%, whereas a similar control without dimmer does not necessarily reduce the lighting electricity demand. However, the results show a considerable variation, making prediction methods as the BAC factor method highly inaccurate. The actual relative energy performance depends on the automation system, type of building, orientation and occupant behaviour (i.e. number of inhabitants and occupancy rate). Hereby, the number of inhabitants has the most considerable impact on the relative energy performances with differences up to 50%, while the occupancy rate shows a significant correlation, especially for low numbers of inhabitants.

Influence of Window Roller Blinds on Energy Consumption in Residential Buildings in Serbia

Abstract Roller blinds are devices that are retracted and wrapped in a box above the window opening and are used to protect against the solar radiation. Those are the wellknown devices with distinct advantages over the fixed external dimming elements. The influence of external blinds on the internal thermal environment in climatic conditions in the Republic of Serbia, the city of Kragujevac, was considered in this research. An external blind was installed in the test room and its influence on the internal temperature was compared with the internal temperature of the room without the blind. Real-time monitoring was carried out in February and August 2023. The results presented in this paper show that the blind reduces the temperature in summer by 3.57ºC and increases it in winter by 1.76ºC. The cooling load can be reduced by about 20%, and the heat load by about 15%.

An Automated Prefabricated Facade Layout Definition for Residential Building Renovation

The rising global energy demand has made it essential to reduce energy consumption in the residential building stock. Adding a new insulating envelope with Renewable Energy Sources (RESs) onto the existing buildings’ facade is one way to achieve zero energy consumption in residential buildings. The ENSNARE project aims to semi-automate this process by using prefabricated facade modules and developing new building data acquisition techniques. Prior to this research project, an analysis was carried out and several research gaps were identified. One of the obstacles to using prefabricated modules with RES is that the layout needs to be drawn and adjusted during different phases of the project. That is time-consuming. For this reason, this article describes two new solutions: (1) automated drafting of the optimized layout of prefabricated modules of the facade and the number of solar panels based on the existing residential building model, and (2) automated adjustment of the layout depending on the phase of the renovation project and the accuracy of the measurements in each step. The proposed semi-automated approach has the potential to significantly reduce the time used in drafting the layout of the prefabricated modules, which benefits the whole renovation process, contributing to a more sustainable future for the residential building stock.

Correlation of AC energy consumption and passive thermal comfort with floor levels in tropical high-rise residential dwellings

The relationship between thermal comfort, building floor levels, and HVAC energy consumption in tropical high-rise residential buildings remains unclear. These buildings face heavy cooling demands due to their immense height and volume, as well as hot-humid weather found in the tropics. Architects and building developers understand that an increase in a building's floor count increases its energy requirements, but the impact on occupant thermal requirements and AC energy consumption needs further investigation. This study aimed to evaluate the relationship between passive thermal comfort, AC energy consumption, and floor levels in tropical high-rise residential buildings through survey and experimental methods. The results showed a significant relationship between thermal sensation votes and building floor levels, as well as AC energy consumption and floor levels. The study also found a strong correlation between thermal sensation votes and predicted mean votes, AC energy use, and predicted people dissatisfied. The optimal cut-off floors for thermal dissatisfaction according to (TSV) are 22nd floor and 8th floor in summer and monsoon seasons respectively. With a 90% sensitivity and 92% specificity. This research highlights the importance of addressing thermal comfort in high-rise buildings to improve energy efficiency and thermal comfort in tropical climates.

Development of a Machine Learning Framework Based on Occupant-Related Parameters to Predict Residential Electricity Consumption in the Hot and Humid Climate

Occupant-related variables constitute one of the most significant groups of factors influencing residential building energy consumption. However, prediction methods often oversimplify these parameters, leading to substantial discrepancies between predicted and actual consumption. To address this issue, the present study aims to develop a machine learning framework for predicting electricity consumption in residential buildings based on occupant-related factors. The study incorporates twenty-six inputs, including occupant characteristics such as demographics, occupancy, behavior, and behavioral efficiency, two time-related factors, and three extra parameters related to equipment (refrigerator age, hot water source, and type of electricity meter) for training and testing the Random Forest (RF) algorithm in both regression and classification forms. The results indicate that the trained RF regressor exhibits well performance (R2Train = 0.989, R2Test = 0.916, MAETrain = 0.81, MAETest = 2.21, RMSETrain = 1.27, and RMSETest = 3.45). Furthermore, feature importance analysis reveals that the most significant parameter is the time of year, representing weather conditions, followed by the number of occupants, neighborhood, indoor set-point range, mean age of occupants, window opening, and cooling system mode. Even after removing the least impactful factors, the model maintains strong performance with the 16 most important variables (R2Train = 0.986, R2Test = 0.910, MAETrain = 0.83, MAETest = 2.25, RMSETrain = 1.31, and RMSETest = 3.49). Additionally, the RF classifier is designed for problems with 2, 4, 6, and 8 classes based on energy consumption ranges. The results of this model demonstrate that the 2-class model achieves the highest performance (AccuracyTest = 0.963, MAETest = 0.04, and RMSETest = 0.19). However, it lacks detailed categorization of homes based on electricity consumption. On the other hand, the 4-class and 6-class models strike a good balance between prediction performance and the level of detail. In conclusion, the proposed method can accurately predict residential electricity consumption and can serve as a valuable reference for researchers and utility managers when formulating energy reduction policies and comparing the effectiveness of different strategies.