Energy Consumption In Residential Buildings Research Articles

Evaluating Energy Consumption in Residential Buildings in Qatar: A Case Study on Compounds

The global urgency to cut carbon emissions and pollution is clear. Qatar, rich in fossil fuels, is shifting towards sustainability to reduce carbon emissions. This paper analyzes the energy consumption patterns in residential buildings in Qatar, categorizing them by size and ownership, and establishing energy benchmarks for each building type, offering insights to guide energy efficiency policies. By examining the building size and ownership, the study helps establish benchmarks, supporting Qatar’s sustainability goals in reducing carbon emissions. The study was conducted from 1 January 2019 to 31 December 2021, utilizing data from the Qatar General Electricity and Water Corporation (KAHRAMAA). A total of 172,796 residential buildings were analyzed, with data on building characteristics and demographic information incorporated into the analysis. A quantitative analysis revealed that the building size, ownership, and demographics significantly impact energy consumption, guiding efficiency strategies. The ownership and floor area significantly impact energy consumption. A strong positive correlation (R = 0.97) was found between energy consumption (kWh) and the total built area (m2). The patterns of energy use varied across different residential building types. The findings highlight the importance of considering the ownership and building size in energy efficiency policies. Identifying specific energy use patterns supports the development of targeted strategies. This research offers valuable data on residential energy consumption in Qatar, providing a foundation for energy benchmarks. These benchmarks can guide policy decisions and strategies to enhance energy efficiency and promote sustainability in the residential sector. This study uniquely connects the ownership and building size with energy consumption patterns in Qatar, supporting the development of effective energy policies and contributing to global sustainability goals.

Application of a decision tree approach to predict energy consumption in lightweight buildings under subtropical climate

PurposeLightweight building systems have emerged as alternatives to reduce the high environmental impact of conventional masonry. However, in subtropical climates, the low thermal inertia of lightweight envelopes negatively affects energy performance. The purpose of this paper is to investigate the thermophysical parameters that influence heating and cooling energy consumption in lightweight residential buildings under subtropical climates and develop a model to predict these parameters using statistical and machine learning tools.Design/methodology/approachA database was created with computer simulation data on the energy performance of 2048 building conditions generated by factorial combination of 10 parameters. Sensitivity analysis was performed to identify which parameters contribute most to energy performance indicators. Subsequently, decision trees were created using a classification and regression tree (CART) algorithm to visualize parameters and improve energy performance indicators, particularly cooling energy consumption.FindingsLow thermal transmittance and ground contact are interesting strategies for low thermal capacity buildings. Furthermore, the findings showed that relying only on the most influential properties does not ensure good energy performance; rather, it is the adequate combination of envelope properties that leads to good energy efficiency. The tree developed by CART can be used as a guide to assist designers and researchers in the initial selection of building envelopes, demonstrating the impact of each choice on electrical energy consumption for indoor climate control.Originality/valueBy adopting a global approach to assess the thermal performance of lightweight buildings, this study makes a significant contribution to synthesizing the results of a complex and time-consuming methodology into a guide for optimizing envelope design decisions and directing efforts and resources toward efficient strategies.

Exploratory Analysis of a Novel Modular Green Wall’s Impact on Indoor Temperature and Energy Consumption in Residential Buildings: A Case Study from Belgium

One possible solution that mitigates the effects of climate change is the implementation of vertical greenery systems, which have the potential to reduce the need for cooling and provide energy savings for heating. This paper evaluates the effects of an innovative modular green wall on indoor temperature and energy use in a residential case study building. This research was carried out on a residential house in the city of Ghent, Belgium, whose southwest facade is covered with a specific type of modular green wall (a structure with a specific substrate and plants that have the ability to purify water so that it can be reused in the house). The monitoring process included four different temperatures (in front of and behind the green wall, in the substrate, and on the wall without greenery) during winter and summer periods. To analyze the effect on the internal temperature and energy use, a DesignBuilder simulation model was built and validated against these experimental results. This green wall has proven to have the greatest effect during the hottest summer days by reducing the indoor temperature by up to 3.5 °C. It also effectively increases the indoor temperature by up to 1.4 °C on a cold winter day, leading to energy savings of 6% on an annual basis.

Simulation-based design: minimizing energy consumption in residential buildings through optimal thermal insulation

Purpose This study aims to optimize the energy consumption of residential buildings in mild and humid climates. It investigates the use of thermal insulation to reduce thermal load through energy simulation analysis. Design/methodology/approach A residential building located in Rasht city, Iran (a mild and humid climate zone), is simulated using DesignBuilder software. Subsequently, the minimum thermal resistance for external walls and roof is analyzed along with its impact on building energy consumption and carbon emissions. Findings The simulation results indicated a 26.5% reduction in heat loss through the walls and a 14.2% reduction through the roof due to optimal thermal insulation. Furthermore, optimal insulation led to a 19.2% reduction in cooling system energy use, a 12% reduction in heating system energy use and a combined 15.3% reduction in total energy consumption for cooling and heating. Originality/value This optimization process leads to several benefits: reduced costs associated with thermal and cooling energy losses in buildings, improved building performance against atmospheric factors and, ultimately, a reduction in energy consumption across the building industry. This research can be valuable to various stakeholders, including the construction industry and building sector, municipalities and engineering systems, building owners and contractors and environmental organizations. By implementing these findings, they can improve the state of modern building insulation and achieve greater energy efficiency.

Optimizing passive energy savings in rural self-built houses: Integrating phase change materials across China's climate zones

The energy consumption of rural residential buildings in China is increasing, and the integration of phase change materials (PCMs) has emerged as an effective strategy to mitigate building energy use and carbon emissions. Given China's diverse climate zones and the varying construction forms of rural self-built houses (RSHs), the efficacy of PCM applications varies significantly across regions. This study presents a comprehensive investigation into passive energy savings using PCMs in RSHs across various climate zones. We investigate the energy saving potential and thermal comfort effects of installing PCMs with varying phase change temperatures and thicknesses at different laying positions in representative buildings from each climate zone. The results indicate that the optimal phase change temperatures in different regions closely align with the outdoor average temperatures during the application season. When the optimal phase change temperatures and appropriate thicknesses of PCM are applied, cost-effective energy savings are consistently achieved in building roofing across regions, with the most significant effects observed in the Severe Cold region, where the uncomfortable degree hours reduction rate (φUDH) exceeds 90 %.

Development and tests of the novel configuration of the solar chimney with sensible heat storage

Heating, ventilation, and air conditioning systems account for a significant part of the energy usage in buildings and are primarily responsible for greenhouse gas emissions and operating costs. These can be lowered by the adoption of solar energy. This paper presents the developing and testing of a novel solar chimney with sensible heat storage. The thermal performance and potential contribution of the developed thermal chimney to decreasing residential buildings energy consumption in Polish climate conditions is investigated. The developed solar air chimney is composed of a novel accumulation material that allows for sensible heat storage. Solar radiation can heat the accumulation layer during the day and the stored energy can be used to preheat the ventilation air. The prototype of the solar chimney was tested in laboratory conditions using a dedicated experimental rig. Furthermore, numerical calculations were performed using TRNSYS software, where a validated dynamic numerical model was developed to simulate the performance of the device. The results show that airflow direction, volume, and the number of heated walls significantly impact the power transferred from the solar chimney walls to the air. The maximum power transferred from the solar chimney walls to the air per unit of surface varied from 360.2 W/m2 (when all walls were heated) to 672.4 W/m2 (when only one wall was heated). The validated model underestimates the outlet air temperature of the solar chimney by a maximum of 0.39 °C on average basis and shows that the increase of air temperature between −10 and 20 °C at 1000 W/m2 determines a decrease of 12.5 % of the thermal output. The obtained results allow one to highlight that the proposed solution can be considered as a replacement for typical brick chimneys located on roofs or as a thermal storage wall.

Developing an integrated prediction model for daylighting, thermal comfort, and energy consumption in residential buildings based on the stacking ensemble learning algorithm

Developing an integrated prediction model for daylighting, thermal comfort, and energy consumption in residential buildings based on the stacking ensemble learning algorithm

Methodology to assess the impact of urban vegetation on the energy consumption of residential buildings. Case study in a Mediterranean city

The global growth of urban areas is unstoppable, and this growth is accompanied by an intensification of urban heat island effects, exacerbating the challenges of climate change and sustainable urban development in warm climates. In this context, understanding the intricate dynamics of these phenomena and their implications on the thermal behaviour of buildings becomes paramount. This study focuses on València, a Spanish city characterized by a Mediterranean climate, where the interplay between ground temperature variations, vegetation levels, and the thermal demands of buildings is investigated.Land surface temperature measurements derived from satellite data, specifically from the Landsat-8 mission, provide a valuable lens through which to assess the heat island effect. These measurements are harmonized with data collected from local weather stations to establish a robust foundation for evaluating the thermal dynamics of the urban environment. European standards, coupled with Geographic Information System technologies, enable the simulation of temperature variations, and facilitate a nuanced analysis of their impact on the thermal demands of a building.Moreover, recognizing the crucial role played by the urban climate in the influencing of heating and cooling needs, this study explores nature-based solutions implemented in València. By leveraging satellite-derived temperature and vegetation data over an extended period, it is possible to identify actions and elements that contribute positively to mitigating UHI effects and improving the overall climatic conditions. Results indicate that vegetation has a notable impact on local temperature, with distinct patterns observed in different seasons. The research incorporated the simulation of climate scenarios, introducing varying levels of vegetation. Results demonstrated a substantial reduction in cooling demand, particularly during the summer months. Buildings with a lower exterior surface-to-volume ratio exhibited a more pronounced reduction in energy consumption.

Spatio-temporal distribution and peak prediction of energy consumption and carbon emissions of residential buildings in China

With the acceleration of urbanization and the improvement of people's living standards, the carbon emissions of residential buildings (BCE) in China have been increasing, hindering the achievement of carbon peaking and carbon neutrality goals. It has become increasingly urgent and important to analyze and grasp the dynamic trends of BCE. Given the notable regional variations in carbon emissions, it is necessary to delve into the distribution characteristics of BCE and the underlying factors influencing them. First, based on the emission factor method, the energy consumption and carbon emissions calculation models are established. Next, the main influencing factors are obtained by improving Kaya's constant equation. Meanwhile, the GA-PPC-Kmeans model is established to divide BCE into different regions. Then, the STIRPAT-Ridge model is built to predict the peak carbon emissions. Finally, a combination of static and dynamic methods is used to forecast peaks under three scenarios, which are low-carbon (LC) scenario, baseline (BA) scenario, and high-carbon (HC) scenario. The results show that five pivotal factors significantly impact BCE in China, which are energy carbon emission intensity (ECEI), energy intensity (EI), economic density (ED), residential housing level (RHL) and population size(P). Furthermore, BCE is categorized into five distinct regions: low-carbon demonstration area, energy efficiency improvement area, carbon concentration and innovation area, high-carbon optimization area, and carbon poverty development area. Under BA scenario, the peaks for BCE in these regions converge primarily around 2040 and 2045. This study provides a regional research perspective for China's residential buildings to reach the peak of carbon emissions, and serves as a reference for formulating carbon emission reduction plans in different regions.

Attaining material circularity in recycled construction waste to produce sustainable concrete blocks for residential building applications

Construction waste poses a major environmental problem owing to traditional building methods, resulting in a huge global waste burden that ultimately ends up in landfills. The development of sustainable methods is necessary not only to reduce environmental problems but also to encourage material circularity and promote the effective recycling and reuse of construction waste. Yet, previous research has largely focused on using recycled construction waste as a single or double additive ingredient. However, this study aims to assess the development of sustainable concrete blocks for residential building applications with a novel blend of two or more waste ingredients, with each additive contributing to a specific characteristic. Physical properties (bulk density, porosity, water absorption, thermal conductivity, compressive strength, fire resistance, and mass loss), cost, and CO2 emissions were determined to evaluate the efficiency of the block. Subsequently, the energy savings were assessed using block samples as single-layer building envelopes within a full-scale room simulated using EnergyPlus. The results showed a reduction in density of up to 24.32 %, and thermal conductivity and heat transfer of up to 56.8 % compared to typical blocks, which satisfied the compressive strength requirements of load- and non-load-bearing walls. The sustainability assessment revealed that the developed concrete blocks could significantly lower production costs by up to 31.75 % and reduce carbon emissions by up to 27.15 % compared to typical concrete blocks. It was found that the best single-layer building envelope in a full-scale residential room improved energy efficiency by up to 9.17 % when the produced blocks were used. This enhancement could lower the cost of energy consumption from 88.34 to 80.24 kWh/m2/year. This research contributes to the utilization of circular building blocks, which reduce the need for typical raw materials while improving their functional properties and lowering the production cost, carbon emissions, and energy consumption in a single-layer residential building.

Determining Payback Period and Comparing Two Small-Scale Vertical Axis Wind Turbines Installed at the Top of Residential Buildings

In recent years, residential buildings have seen a notable increase in energy consumption. To address this, it is crucial for researchers to invest in renewable energy technologies, aiming to develop highly sustainable and nearly-zero energy buildings. Many countries are started to commit to this goal, seeking to phase out fossil fuels due to their harmful environmental effects. Wind energy stands out as a promising renewable resource, especially in areas with strong wind patterns. This study focuses on a case in Karaburun, Izmir province, Türkiye, where annual wind speeds range from 6 to 8 m/s and evaluates the performance of two types of small-scale Vertical Axis Wind Turbines (VAWTs) in reducing energy consumption in a three-story residential building, along with associated costs. Utilizing advanced simulation tools like ANSYS Fluent and DesignBuilder Software, the study examines Ice-Wind VAWTs and Savonius VAWTs. The findings reveal that installing 15 Ice-Wind VAWTs on the building's roof can reduce energy consumption by approximately 22.5%, with each turbine costing about $2000 and a payback period of around 14.57 years. Conversely, using 15 Savonius VAWTs can reduce energy consumption by 36%, with each turbine costing about $2300 and a payback period of around 8.93 years. These results indicate that the Savonius turbine offers a faster return on investment compared to the Ice-Wind turbine under the specified conditions. Overall, this study highlights the significant benefits and cost implications of integrating renewable energy solutions like VAWTs into residential buildings.

Technical and Economica Feasibility of Photovoltaic Tiles Applies to a Single-Family Residence in Belo Horizonte-MG

Objective: The objective of this study is to verify whether photovoltaic tile technology presents technical and economic feasibility to be implemented in a single-family residence located in Belo Horizonte-MG. Theoretical framework: Through the photoelectric effect, photovoltaic tiles directly convert solar energy into electrical energy in a static, silent, non-polluting and renewable way. In this system, photons, energy-carrying particles present in sunlight, fall on a surface composed of photovoltaic cells (made of semiconductor material). At the ends of the semiconductor material, an electrical potential difference appears, responsible for setting the electrons in the structure in motion, producing electric current. Methodology: The methodology adopted for this research consists of simulating the energy consumption of a 77m2 residential building, consisting of 3 members. The tile and photovoltaic panel systems were designed to meet the energy demands of this building. Results and discussion: The results indicated that both systems present technical feasibility for their implementation, since the technologies demonstrated that they are capable of satisfying 100% of energy needs. However, from an economic point of view, only photovoltaic modules were viable. Originality/value: This work makes a contribution to the literature by analyzing a technology that is still little known in the energy market. Implications of the research: The relevance and value of this research are evidenced by the fact that photovoltaic tiles are a sustainable alternative to generate clean energy and reduce greenhouse gas emissions, which can contribute to the diversification of the Brazilian energy matrix and mitigation of impacts. of civil construction.

МОДЕЛЮВАННЯ ЕНЕРГОЕФЕКТИВНОСТІ ЖИТЛОВОЇ БУДІВЛІ ЗА УМОВ РІЗНИХ ДЖЕРЕЛ ТЕПЛОВОЇ ЕНЕРГІЇ

The paper presents the results of modeling the process of energy consumption in public, residential, and commercial buildings as a result of the use of various sources of thermal energy. Buildings with such energy sources as: a) a low-temperature gas boiler with a capacity of up to 120 kW for the needs of heat supply of a residential building and freon air conditioning systems for the needs of cooling the building; b) a gas condensing boiler with a thermal capacity of up to 120 kW with a temperature regime of 55/45°C for heat supply and freon air conditioning systems for cooling; c) reversible heat pumping unit of the “ground-water” type with a temperature regime of 35/28°C for the needs of the heating and cooling system and a reversible heat pumping machine with a regime of 55/45°C for the needs of hot water supply; d) a reversible air-to-air heat pump for heating and cooling systems and electric boilers for hot water supply; e) a biomass solid fuel boiler with manual fuel loading with a heat output of more than 100 kW for heat supply and freon systems for cooling; f) a fuel pellet boiler with automatic mechanized fuel loading with a heat output of more than 100 kW for building heating and cooling and freon systems for cooling; g) centralized heating system of the building. The results of calculations of the specific primary energy consumption and specific greenhouse gas emissions for each of the above heat sources are presented. It is established that biomass boilers have the lowest values of primary energy consumption and specific greenhouse gas emissions. The lowest efficiency is achieved by a district heating system with a centralized energy source. The modeling was conducted for the climatic conditions of Vinnytsia. At the same time, the disadvantage of using solid fuel boilers is the limitation of their use in densely populated areas, which is explained by the complexity of flue gas cleaning and regular biomass supply. Therefore, it has been determined that the most efficient source of thermal energy for buildings of various types to achieve the standardized energy efficiency indicators is a ground-to-water heat pump system. This installation, along with providing buildings with thermal energy, allows for cooling in the warm season, operating in reverse mode.

Energy Consumption Prediction in Residential Buildings—An Accurate and Interpretable Machine Learning Approach Combining Fuzzy Systems with Evolutionary Optimization

This paper addresses the problem of accurate and interpretable prediction of energy consumption in residential buildings. The solution that we propose in this work employs the knowledge discovery machine learning approach combining fuzzy systems with evolutionary optimization. The contribution of this work is twofold, including both methodology and experimental investigations. As far as methodological contribution is concerned, in this paper, we present an original designing procedure of fuzzy rule-based prediction systems (FRBPSs) for accurate and transparent energy consumption prediction in residential buildings. The proposed FRBPSs are characterized by a genetically optimized accuracy–interpretability trade-off. The trade-off optimization is carried out by means of multi-objective evolutionary optimization algorithms—in particular, by our generalization of the well-known strength Pareto evolutionary algorithm 2 (SPEA2). The proposed FRBPSs’ designing procedure is our original extension and generalization (for regression problems operating on continuous outputs) of an approach to designing fuzzy rule-based classifiers (FRBCs) we developed earlier and published in 2020 in this journal. FRBCs operate on discrete outputs, i.e., class labels. The experimental contribution of this work includes designing the collection of FRBPSs for residential building energy consumption prediction using the data set published in 2024 and available from Kaggle Database Repository. Moreover, the comparison with 20 available alternative approaches is carried out, demonstrating that our approach significantly outperforms alternative methods in terms of interpretability and transparency of the energy consumption predictions made while remaining comparable or slightly superior in terms of the accuracy of those predictions.

Spatial distribution of energy consumption: Integrating climate and macro-statistics for insights from clustering and sensitivity analysis

Limited comprehensive methods exist for studying spatial energy consumption distribution, integrating statistical and energy data. This paper introduces a novel approach for analyzing residential heating and cooling energy demand distribution. It employs clustering algorithms to study climate variables’ impact on energy demand distribution and assesses building energy demand intensity regionally, taking China as a case study. Initially compiling a dataset comprising climate characteristics, socioeconomic factors, and energy demand through data collection and simulation, the study compares clustering algorithms, highlighting the effectiveness of K-means in clustering high-dimensional climate-energy datasets. K-means analysis reveals temperature-based daily methods significantly affect building energy intensity, alongside factors like radiation intensity and humidity impacting regional energy demand variably. Additionally, climate’s influence on residential building energy consumption intensity varies regionally, with total energy demand influenced by population and economic factors. This paper offers insights for energy management and policy formulation.

Examining the Impact of Natural Ventilation versus Heat Recovery Ventilation Systems on Indoor Air Quality: A Tiny House Case Study

Adverse health effects can arise from indoor air pollutants, resulting in allergies, asthma, and other respiratory problems among occupants. Concurrently, the energy consumption of residential buildings, particularly concerning heating, ventilation, and air conditioning (HVAC) systems, significantly contributes to global energy usage. To address these intertwined challenges, heat recovery ventilation (HRV) has emerged as a viable solution to reduce heating and cooling demands while providing fresh ventilation rates. This study aims to investigate the indoor air quality (IAQ) of an experimental tiny house building equipped with an HRV unit by simulating real-life scenarios contributing to IAQ. The research evaluates the effectiveness of HRV compared to natural ventilation in managing particle matter (PM), total volatile organic compounds (TVOC), formaldehyde (CH2O), carbon monoxide (CO), and carbon dioxide (CO2) levels. This research significantly contributes to the understanding of the different ventilation strategies’ impact on IAQ in tiny houses and offers valuable insights for improving living conditions in a unique building typology that is underrepresented in the research literature.

Estimating the energy consumption for residential buildings in semiarid and arid desert climate using artificial intelligence

This research aims to develop predictive models to estimate building energy accurately. Three commonly used artificial intelligence techniques were chosen to develop a new building energy estimation model. The chosen techniques are Genetic Programming (GP), Artificial Neural Network (ANN), and Evolutionary Polynomial Regression (EPR). Sixteen energy efficiency measures were collected and used in designing and evaluating the proposed models, which include building dimensions, orientation, envelope construction materials properties, window-to-wall ratio, heating and cooling set points, and glass properties. The performance of the developed models was evaluated in terms of the RMS, R2, and MAPE. The results showed that the EPR model is the most accurate and practical model with an error percent of 2%. Additionally, the energy consumption was found to be mainly governed by three factors which dominate 87% of the impact; which are building size, Solar Heating Glass Coefficient (SHGC), and the target inside temperature in summer.

A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods

The considerable amount of energy utilized by buildings has led to various environmental challenges that adversely impact human existence. Predicting buildings' energy usage is commonly acknowledged as encouraging energy efficiency and enabling well-informed decision-making, ultimately leading to decreased energy consumption. Implementing eco-friendly architectural designs is paramount in mitigating energy consumption, particularly in recently constructed structures. This study utilizes clustering analysis on the original dataset to capture complex consumption patterns over various periods. The analysis yields two distinct subsets that represent low and high consumption patterns and an additional subset that exclusively encompasses weekends, attributed to the specific behavior of occupants. Ensemble models have become increasingly popular due to advancements in machine learning techniques. This research utilizes three discrete algorithms, namely Artificial Neural Network (ANN), K-nearest neighbors (KNN), and Decision Trees (DT). In addition, the application employs three more machine learning algorithms bagging and boosting: Random Forest (RF), Extreme Gradient Boosting (XGB), and Gradient Boosting Trees (GBT). To augment the accuracy of predictions, a stacking ensemble methodology is employed, wherein the forecasts generated by many algorithms are combined. Given the obtained outcomes, a thorough examination is undertaken, encompassing the techniques of stacking, bagging, and boosting, to conduct a comprehensive comparative study. It is pertinent to highlight that the stacking technique consistently exhibits superior performance relative to alternative ensemble methodologies across a spectrum of heterogeneous datasets. Furthermore, using a genetic algorithm enables the optimization of the combination of base learners, resulting in a notable enhancement in prediction accuracy. After implementing this optimization technique, GA-Stacking demonstrated remarkable performance in Mean Absolute Percentage Error (MAPE) scores. The improvement observed was substantial, surpassing 90 percent for all datasets. In addition, in subset-1, subset-2, and subset-3, the achieved R2 scores were 0.983, 0.985, and 0.999, respectively. This represents a substantial advancement in forecasting the energy consumption of residential buildings. Such progress underscores the potential advantages of integrating this framework into the practices of building designers, thereby fostering informed decision-making, design management, and optimization prior to construction.

Analyzing the opening and closing of windows in residential for predicting the energy consumption using optimized multi-scale convolution networks

PurposeThis proposal aims to forecast energy consumption in residential buildings based on the effect of opening and closing windows by the deep architecture approach. In this task, the developed model has three stages: (1) collection of data, (2) feature extraction and (3) prediction. Initially, the data for the closing and opening frequency of the window are taken from the manually collected datasets. After that, the weighted feature extraction is performed in the collected data. The attained weighted feature is fed to predict energy consumption. The prediction uses the efficient hybrid multi-scale convolution networks (EHMSCN), where two deep structured architectures like a deep temporal context network and one-dimensional deep convolutional neural network. Here, the parameter optimization takes place with the hybrid algorithm named jumping rate-based grasshopper lemur optimization (JR-GLO). The core aim of this energy consumption model is to predict the consumption of energy accurately based on the effect of opening and closing windows. Therefore, the offered energy consumption prediction approach is analyzed over various measures and attains an accurate performance rate than the conventional techniques.Design/methodology/approachAn EHMSCN-aided energy consumption prediction model is developed to forecast the amount of energy usage during the opening and closing of windows accurately. The emission of CO2 in indoor spaces is highly reduced.FindingsThe MASE measure of the proposed model was 52.55, 43.83, 42.01 and 36.81% higher than ANN, CNN, DTCN and 1DCNN.Originality/valueThe findings of the suggested model in residences were attained high-quality measures with high accuracy, precision and variance.

Development of bio-based lightweight and thermally insulated bricks: Efficient energy performance, thermal comfort, and CO2 emission of residential buildings in hot arid climates

This study explores the viability of using industrial wastewater sludge (IWWS), generated by the mycelium treatment of dyeing wastewater, for the fabrication of lightweight and thermally insulating bricks. The pulverized IWWS was used at various replacement ratios of 0, 5, 10, 15, and 20 wt% of clay. The mixture was subjected to firing temperatures of 700, 800, and 900 °C. The effect of applying IWWS to the brick characteristics is assessed through an examination of the physico-mechanical and thermal properties. The utilization of IWWS resulted in a significant enhancement in apparent porosity (34.4 %), cold water absorption (25.6 %), boiling water absorption (28.5 %), linear drying shrinkage (5.1 %), and linear firing shrinkage (7.9 %) using 20 % IWWS at 900 °C. Furthermore, the density, compressive strength, and thermal conductivity were reduced as the substitution IWWS ratio increased, reaching up to 1662.9 kg/m3, 11.56 MPa, and 0.198 W/m.K., respectively, using 20 wt% IWWS at 900 °C. The effect of fabricated fired bricks (FBs) on the thermal insulation, CO2 emissions, and energy consumption of residential buildings was tested. This is accomplished by using energy modeling tools, such as Honeybee, Ladybug, and Climate Studio (Grasshopper plugins), in addition to Galapagos for optimizing wall design. The suggested wall design is being used in a communal residential building located in two distinct climatic regions, namely Cairo in Egypt and Jazan in Saudi Arabia. According to the projected mean vote (PMV) and anticipated percentage of discontent (PPD) measures, the utilization of fabricated FBs as wall materials exhibits significant improvements in thermal comfort. Furthermore, they lead to a 6 % and 7 % drop in CO2 emissions in Cairo and Jazan cities, respectively, as well as a significant 38.8 % and 39 % reduction in energy consumption in Cairo and Jazan cities, respectively. Therefore, the use of fabricated FBs demonstrates itself as a feasible and environmentally friendly choice for building construction.