Energy Performance Of Residential Buildings Research Articles

Sustainable-Driven Renovation of Existing Residential Buildings in China: A Systematic Exploration Based on Review and Solution Approaches

Under the backdrop of China’s national strategy to achieve carbon neutrality by 2060, efforts are underway across governmental, corporate, societal, and individual sectors to actively explore energy-saving renovations in existing buildings. Given that residential buildings constitute a significant proportion of the total energy consumption throughout the lifecycle of buildings in China, sustainable renovation of residential structures can contribute significantly to implementing China’s carbon emission reduction policies. While there exists a plethora of technological means in the market aimed at improving the energy performance of residential buildings, there still needs to be a more systematic discussion on the framework for sustainable renovation of existing Chinese residential buildings, with knowledge dissemination still needing to be more cohesive. In this context, this paper provides a comprehensive review of the field, utilizing bibliometric methods. Through a systematic review of selected peer-reviewed literature from the Web of Science and Scopus databases, the study focuses on the sustainable renovation of existing Chinese residential buildings, categorizing the process into three main stages: sustainable renovation, building performance simulation and suitability assessment. The paper also reviews the research methods adopted by previous researchers in the renovation, simulation and assessment stages, considering various optimization algorithms, variables, objectives, and software tools. Subsequently, the paper synthesizes a research framework comprising these three stages combined for different research objectives, aiming to assist policymakers, designers, and researchers in gaining a comprehensive understanding of the implementation status of sustainable renovation in existing Chinese residential buildings, identifying barriers to implementation, and formulating more efficient renovation policies and strategies for the future.

A Comparative Study on Discrepancies in Residential Building Energy Performance Certification in a Mediterranean Context

Energy Performance Certification (EPC) systems are pivotal in addressing the global energy challenge, particularly in the building sector. This study evaluates the efficacy of the EPC offered by the Simplified Building Energy Model interface designed to indicate compliance with the Cypriot building regulations, widely known as iSBEM-Cy Version 3.4a, by examining a typical residential unit in Cyprus. Data on construction features and electromechanical systems were collected, and actual monthly electricity and oil bills were analyzed to determine the total primary energy consumption. Various factors were considered, including energy efficiency and operational parameters for heating, cooling, lighting, auxiliary systems, and domestic hot water. The building energy performance was simulated using iSBEM-Cy, allowing for comparison with real-world energy consumption. Notable discrepancies were observed, particularly in cooling, with deviations reaching 377.4%. Conversely, domestic hot water consumption exhibited minimal variance at 7%, while heating and lighting showed moderate discrepancies (24.3% and −113.9%, respectively). This study underscores the need for rigorous evaluations to shape effective EPC and provides insights into building energy performance in Mediterranean Cyprus. This research contributes to the broader discourse on sustainable construction practices by aligning simulation results with real-world energy consumption.

Prediction of energy performance of residential buildings using regularised neural models

Human habitats are one of the major consumers of energy. Therefore, in the current age of increasing carbon dioxide footprints, analysing energy efficiency of a building is important and is the subject of the current study. Machine-learning-based artificial neural network (ANN) approaches are used in the current study to investigate building energy performance. Eight parameters – relative compactness, surface area, wall area, roof area, overall height and orientation of the building, as well as the glazing area and its distribution – are selected as the input parameters and heating and cooling loads (CLs) as the output parameters. The network prediction capability was checked by comparing the predictions of the ANN architecture with the benchmark test case. A well-trained and validated ANN is used to predict 96 conditions by varying glazing area and glazing area distribution. The ANN is found to capture the physics efficiently. This study revealed that there is a significant potential to improve the energy efficiency of a building and the maximum saving in the CL can be as high as 20.67% for a fraction of the glazing areas equal to 0.15 if the glazing area distribution is 32.5% in the north and 22.5% each in the east, south and west.

Cracking the code: mapping residential building energy performance in rural Central Asia through building typologies

Buildings in rural Central Asia have unique characteristics as they were built during the Soviet era (during 1950–1960) without any energy efficiency measures. The special and aged building stock pose a crucial challenge on energy security and energy supply. However, accurate scientific data on their energy usage is lacking, highlighting a research gap about robust and validated methodology to determine the energy use of rural Central Asian buildings. In response to this need, this research paper proposes a set of generalised residential building typologies for rural Central Asia, with a focus on Kyrgyzstan. The study combines quantitative and qualitative methods, including household surveys, energy demand modelling, EnergyPlus simulations, and result validation. It derives energy-based building typologies from construction year and building envelope characteristics. Through numerous validated simulations, it was identified that the heat demand for rural Kyrgyz houses is 3–5 times here compared to European houses (250–400 kWh/m2). The study revealed the estimated heat demand for homes in rural Kyrgyzstan, indicating a significant potential for improving building energy efficiency in the region. These results can inform specific approaches, such as building renovation plans, energy certification, and renovation strategies. Furthermore, the validated methodology introduced offers opportunities for researchers in the field of building energy efficiency and can be applied to similar regions in Central Asia with comparable building stocks and climates.

Multi-Objective Decision-Making Tool for Envelope Energy Retrofitting Measures of Gated Community Housing in Egypt

Due to climate change, Egypt has recently suffered from recurring electricity crises. Despite efforts made to increase electricity production in Egypt, recently, in the summer months, the energy demand has increased at unprecedented rates, especially in the housing sector. Therefore, the government and homeowners should work together to improve the energy performance of residential buildings. This paper aimed to develop a decision-making tool that helps homeowners choose optimal energy retrofit measures that suit their priorities. The study began with the data-collection and case study selection. Then, the thermal evaluation of the base case for dwellings in the case study was conducted through simulation runs using the DesignBuilder v7.1 software. Then, the optimal envelope energy retrofitting measures were determined, followed by a retrofitting-measure scenario simulation process. Then, the payback periods were calculated for all scenarios, and the tool database was developed using an Excel spreadsheet. Finally, the user interface for envelope energy retrofitting measures for gated communities (EERMGCs) tool was designed by Visual Basic for Applications. EERMGCs, the tool developed in this paper, is a simple, multi-objective and interactive tool that provides the optimal envelope retrofit measures according to user priorities, either a specific budget, the shortest payback period, the lowest possible costs, or the highest energy saving rate. The outcome of this research is developing a framework that can be considered a basis for developing decision-making tools for gated community housing in Egypt.

Statistical Building Energy Model from Data Collection, Place-Based Assessment to Sustainable Scenarios for the City of Milan

Building energy modeling plays an important role in analyzing the energy efficiency of the existing building stock, helping in enhancing it by testing possible retrofit scenarios. This work presents an urban scale and place-based approach that utilizes energy performance certificates to develop a statistical energy model. The objective is to describe the energy modeling methodology for evaluating the energy performance of residential buildings in Milan; in addition, a comprehensive reference dataset for input data from available open databases in Italy is provided—a critical step in assessing energy consumption and production at territorial scale. The study employs open-source software QGIS 3.28.8 to model and calculate various energy-related variables for the prediction of space heating, domestic hot water consumptions, and potential solar production. By analyzing demand/supply profiles, the research aims to increase energy self-consumption and self-sufficiency in the urban context using solar technologies. The presented methodology is validated by comparing simulation results with measured data, achieving a Mean Absolute Percentage Error (MAPE) of 5.2%, which is acceptable, especially considering city-scale modeling. The analysis sheds light on key parameters affecting building energy consumption/production, such as type of user, volume, surface-to-volume ratio, construction period, systems’ efficiency, solar exposition and roof area. Additionally, this assessment attempts to evaluate the spatial distribution of energy-use and production within urban environments, contributing to the planning and realization of smart cities.

Effect of Balcony’s Design Parameters on Energy Performance of Residential Buildings

Effect of Balcony’s Design Parameters on Energy Performance of Residential Buildings

Prediction of building energy performance using mathematical gene-expression programming for a selected region of dry-summer climate

Developing energy-efficient buildings considering building design parameters can help reduce buildings' energy consumption. The energy efficiency of residential buildings is considered a top priority for the energy policies of a country. Thus, this study utilizes gene-expression programming (GEP) to estimate the energy performance of residential buildings. The energy consumption evaluations were carried out using the Etotect energy simulation software. Eight building parameters with 768 data points were considered to generate the database for the heating load (HL) and cooling load (CL), including relative compactness, surface area, wall area, roof area, overall building height, glazing orientation, glazing area, and distribution of glazing area. Different GEP predictive models with varying parameters for building HL and CL were developed, and the best-performing prediction model was selected. In addition, several statistical indices were utilized to measure the accuracy of the proposed GEP models. The results revealed that GEP14 gives the most robust prediction model for HL having R2-value greater than 0.9 for both the training and validation set. Likewise, R2-value >0.9 is achieved for best- CL (GEP11). Furthermore, the mean absolute error (MAE) values for both the predictive HL’ and CL’ by prediction models were relatively less for both the training and testing databases. The sensitivity and parametric analysis reveal that the overall height (Ho), roof area (Af), and glazing area (Ag) were the most influential parameters for both predictive models. Thus, GEP results demonstrate the robust performance in predicting the building energy.

Evaluation of climate change effects on residential building cooling and heating demands in New Zealand: implications for energy efficiency standards and building codes

PurposeClimate change reports from New Zealand claim that climate change will impact some cities such as Auckland from a heating-dominated to a cooling-dominated climate. The benefits and risks of climate change on buildings' thermal performance are still unknown. This paper examines the impacts of climate change on the energy performance of residential buildings in New Zealand and provides insight into changes in trends in energy consumption by quantifying the impacts of climate change.Design/methodology/approachThe present paper used a downscaling method to generate weather data for three locations in New Zealand: Auckland, Wellington and Christchurch. The weather data sets were applied to the energy simulation of a residential case study as a reference building using a validated building energy analysis tool (EnergyPlus).FindingsThe result indicated that in Wellington and Christchurch, heating would be the major thermal load of residential buildings, while in Auckland, the main thermal load will change from heating to cooling in future years. The revised R-values for the building code will affect the pattern of dominant heating and cooling demands in buildings in Auckland in the future, while in Wellington and Christchurch, the heating load will be higher than the cooling load.Originality/valueThe findings of this study gave a broader insight into the risks and opportunities of climate change for the thermal performance of buildings. The results established the significance of considering climate change in energy performance analysis to inform the appropriate building codes for the design of residential buildings to avoid future costly changes to buildings.

An Energy-Saving-Oriented Approach to Urban Design—Application in the Local Conditions of Poznań Metropolitan Area (Poland)

This article discusses the impact of urban layout on the energy performance of residential buildings. A comparative analysis of multiple variants of land development differing in building layout only, with all other features being the same, including the building envelope properties and technical equipment, was carried out. The research was conducted in two selected locations in Poznań metropolitan area (Poland), which is located in the Dfb climate zone (humid continental—warm summer subtype). For each location, the following variants of building layout were considered: parallel buildings (12 variants with an orientation towards the sides of the world rotated in steps of 15 degrees), perimeter frontage buildings, and comb-shaped buildings with semi-open courtyards (4 variants with courtyards open to each side of the world). The calculation of annual end uses for heating and cooling was conducted as well as the peak values. All calculations were performed using OpenStudio Application Release v. 1.2.1 software with the SketchUp plugin. The results showed that the proper arrangement of buildings on the urban plot may result in significant energy savings. The considered variants differed in terms of annual end uses for heating and cooling even by approx. 15%, and the peak values on the hottest days were 4–10 times lower in comparison with the least advantageous variants. The results show the slight advantage of compact development over free-standing development in terms of total end uses as well as the south and north orientation of facades over the east and west in terms of peak solar heat gain values.

The influence of the urban heat island effect on the energy performance of residential buildings in a city with an oceanic climate during the summer period: Case of Valdivia, Chile

The intensification of urban growth and climate change are affecting the energy and thermal performance of buildings. In recent years, the issue of building resilience to urban heat island (UHI) conditions has become increasingly relevant. The energy performance of buildings can vary significantly in different areas of the same city, regardless of their size. The aim of this study was to evaluate the energy effects of UHI intensity differences in various local climate zones (LCZs) of Valdivia city, Chile, on a typical residential dwelling. A simplified methodology was used, based on the assessment of cooling degree days variations and heat gains variations inside the studied house through the thermal envelope. Valdivia has a homogeneous urban morphology, and three types of low-rise LCZs prevail in the city (LCZ 3, 6, and 9). The results showed that the average cooling demand for a 66 m2 residential dwelling during 39 summer days was 158 kWh for LCZ 9, 219 kWh for LCZ 6, and 289 kWh for LCZ 3, compared to the rural demand of 114 kWh. These results suggest that the energy effects of UHI can be significant, and that it is important to study the microclimatic conditions in various LCZs for a correct understanding of the UHI energy effects on different buildings.

An Integrated Decision-Making Framework for Mitigating the Impact of Urban Heat Islands on Energy Consumption and Thermal Comfort of Residential Buildings

Urban heat island (UHI) is a zone that is significantly warmer than its surrounding rural zones as a result of human activities and rapid and dense urbanization. Excessive air temperature due to the UHI phenomenon affects the energy performance of buildings and human health and contributes to global warming. Knowing that most of the building energy is consumed by residential buildings, therefore, developing a framework to mitigate the impact of the UHI on residential building energy performance is vital. This study develops an integrated framework that combines hybrid micro-climate and building energy performance simulations and multi-criteria decision-making techniques. As a case study, an urban area is analyzed under the Urban GreenUP project funded by the European Union’s Horizon 2020 Programme. Four different strategies to mitigate the UHI effect, including the current situation, changing the low-albedo materials with high-albedo ones, nature-based solutions, and changing building façade materials, are investigated with a micro-climatic simulation tool. Then, the output of the strategies, which is potential air temperature, is used in a dynamic building energy simulation software to obtain energy consumption and thermal comfort data of the residential buildings in the case area. Finally, a multi-criteria decision-making model, using real-life criteria, such as total energy consumption, thermal comfort, capital cost, lifetime and installation flexibility, is used to make a decision for decreasing the UHI effect on residential energy performance of buildings. The results showed that applying NBSs, such as green roofs and changing existing trees with high leaf area density ones, have the highest ranking among all mitigation strategies. The output of this study may help urban planners, architects, and engineers in the decision-making processes during the design phase of urban planning.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change.

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization. Urban building energy modeling (UBEM) is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations, supporting the implementation of carbon emission reduction policies. Currently, most studies focus on the energy performance of archetype buildings under climate change, which is hard to obtain refined results for individual buildings when scaling up to an urban area. Therefore, this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas, by taking two urban neighborhoods comprising 483 buildings in Geneva, Switzerland as case studies. In this regard, GIS datasets and Swiss building norms were collected to develop an archetype library. The building heating energy consumption was calculated by the UBEM tool-AutoBPS, which was then calibrated against annual metered data. A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%. The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results showed a decrease of 22%-31% and 21%-29% for heating energy consumption, an increase of 113%-173% and 95%-144% for cooling energy consumption in the two neighborhoods by 2050. The average annual heating intensity dropped from 81 kWh/m2 in the current typical climate to 57 kWh/m2 in the SSP5-8.5, while the cooling intensity rose from 12 kWh/m2 to 32 kWh/m2. The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7% and 18.6%, respectively, in the SSP scenarios. The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

Improving indoor thermal and energy performance of large-space residential buildings via active approaches

Improving the indoor thermal and energy performance of residential buildings plays a significant role in the low-carbon goals of building industry. Residential buildings usually utilize the active approaches, such as air conditioning and radiant floor. A large-space residential building that removes partial second floor can employ the retractable membrane ceiling to control building indoor performance. These active approaches can be combined to ensure thermal comfort and save energy of large-space residential buildings. Therefore, this study carries out a series of experiments to quantify the individual and combined effects of retractable membrane ceiling, air conditioning and radiant floor.The dual-approach and triple-approach experiments combine two and three from the retractable membrane ceiling, air conditioning and radiant floor. It is obtained that the membrane ceiling/air conditioning can effectively improve indoor thermal performance. The energy consumptions for air conditioning and membrane ceiling/air conditioning are 1.14 kWh and 0.89 kWh at an air temperature of 25 °C. The triple-approach experiments can solve the local thermal discomfort caused by the low air movement since the radiant floor accelerates the local air movement. The comparisons of temperature difference, air velocity and energy consumption for single-approach, dual-approach and triple-approach demonstrate that the retractable membrane ceiling is an efficient method to improve indoor thermal and energy performance of large-space residential buildings.

Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance

Proper analysis of building energy performance requires selecting appropriate models for handling complicated calculations. Machine learning has recently emerged as a promising effective solution for solving this problem. The present study proposes a novel integrative machine learning model for predicting two energy parameters of residential buildings, namely annual thermal energy demand (DThE) and annual weighted average discomfort degree-hours (HDD). The model is a feed-forward neural network (FFNN) that is optimized via the electrostatic discharge algorithm (ESDA) for analyzing the building characteristics and finding their optimal contribution to the DThE and HDD. According to the results, the proposed algorithm is an effective double-target model that can predict the required parameters with superior accuracy. Moreover, to further verify the efficiency of the ESDA, this algorithm was compared with three similar optimization techniques, namely atom search optimization (ASO), future search algorithm (FSA), and satin bowerbird optimization (SBO). Considering the Pearson correlation indices 0.995 and 0.997 (for the DThE and HDD, respectively) obtained for the ESDA-FFNN versus 0.992 and 0.938 for ASO-FFNN, 0.926 and 0.895 for FSA-FFNN, and 0.994 and 0.995 for SBO-FFNN, the ESDA provided higher accuracy of training. Subsequently, by collecting the weights and biases of the optimized FFNN, two formulas were developed for easier computation of the DThE and HDD in new cases. It is posited that building engineers and energy experts could consider the use of ESDA-FFNN along with the proposed new formulas for investigating the energy performance in residential buildings.

Energy Performance Aspects of Residential Buildings in Latvia

Abstract Measures on increasing energy efficiency is a way for ensuring sustainable energy supply, reducing emission of gases causing the greenhouse effect, improving safety of energy supply, decreasing dependence on import of energy and promoting the EU competitiveness. Improvement of energy performance of buildings as a direction of activities is provided in national policy planning documents of major importance such as the National Energy and Climate Plan for 2021–2030 and the Strategy of Latvia for the Achievement of Climate Neutrality by 2050. Residential buildings form a considerable part of the total Latvian housing stock – 27 % according to the number of buildings and 44 % according to their area. Therefore, the increase of energy performance of residential buildings is very significant for achieving the national energy saving targets, i.e. a cumulative savings of final energy consumption of about 6 PJ by 2030. The research analyses the sector of residential buildings, their statistical data, the energy consumption and requirements of the energy performance standards. To characterise this sector more comprehensively, information has been evaluated on energy performance certificates of residential buildings issued during the period from 2016 to 2021, allowing the authors to make general conclusions on the energy performance level of residential buildings and to provide proposals for increasing their energy efficiency.

Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

Impact of Location and Insulation Material on Energy Performance of Residential Buildings as per Saudi Building Code (SBC) 601/602 in Saudi Arabia

In hot and humid climates, a significant part of the energy is used to cool the building. There are several ways to reduce this air conditioning load, but one standout is through the selection and design of the right building envelope and its components. The thermal characteristics of the building envelope, in particular the thermal resistance of the insulation used, have an impact on the thermal and energy performance of building structures. Thermal conductivity, which indicates the ability of heat to move through a material given a temperature difference, is the primary factor affecting the performance of a thermal insulation material. Both temperature and humidity changes can affect a material's thermal conductivity value, which can then change. In fact, due to the fluctuating ambient air temperature and solar radiation, thermal insulation in buildings is susceptible to significant and continuous temperature variations. Thermal insulation used in building walls and roofs helps to reduce the energy demand of the building. It improves thermal comfort and, if used correctly, reduces the operational cost of the building. The present study has focused on the effects of location and insulation material on the energy performance of a residential building by considering five climatic locations in the Kingdom of Saudi Arabia (KSA). Five commonly used insulation materials with different thermal characteristics, namely polyurethane board (PU), expanded polystyrene (EPS), glass wool (GW), urea-formaldehyde foam (UFF), and expanded perlite (EP), were analyzed under various climatic zones as per the Saudi Building Code 601/602. The selected cities were categorized based on cooling degree days (CDD) and outdoor dry bulb temperature (DBT) as hot, very hot, and extremely hot climatic zones. Insulation improves thermal comfort and, if used correctly, reduces running costs. Experiments were conducted to determine the thermal conductivity, and the energy simulation was performed by employing IES-VE software for various insulation options. The findings indicate that the location has a significant impact on the energy performance of the insulating materials. The energy saving potential of polyurethane board (PU) insulation is more attractive in cities with higher DBTs and CDDs than in cities with lower DBTs and CDDs. The benefit of installing insulation ranged from a 2 to 14% decrease in energy demand for the climate zones studied. The sensitivity analysis showed that the energy saving potential of the insulation materials is sensitive to the set-point temperature (ST) band.

Impact of Dynamic Slab Insulation on Energy Performance of Residential Buildings

Abstract In this paper, the energy efficiency potential of applying novel dynamic insulation systems to slab foundations is investigated for residential buildings. Specifically, dynamic insulation allows the foundation to change its thermal resistance to reduce both heating and cooling thermal loads compared to static insulation systems. The energy benefits of the dynamic insulation are evaluated using a validated numerical model integrated with a state-of-art whole-building simulation tool. Specifically, optimal settings for slab-integrated dynamic insulation are determined monthly to reduce heating and cooling thermal loads while maintaining thermal comfort for a prototypical house located in representative US climates. The analysis results indicate that the deployment of slab-integrated dynamic insulation can reduce heating energy by 10% and cooling energy by 39%, and total heating, ventilating, and air conditioning end-use by up to 12%, especially for houses located in cold climates.

Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence

Energy consumption prediction is an integral part of planning and controlling energy used in the building sector which accounts for 40% of the global energy consumption and a significant portion of greenhouse gas emissions. However, very few studies focused on the combined effect of building characteristics, building geometry, and urban morphology on energy performance. Such a research gap is addressed in this study by developing an explainable deep learning model. Our model uses Light Gradient Boosting Machine integrated with the SHapley Additive exPlanation algorithm, so as to provide insights into the feasibility of using machine learning-based models for energy performance prediction of buildings. With the proposed eXplainable Artificial Intelligence model, this study successfully predicts energy usage and greenhouse gas emissions of residential buildings, as well as identifies the most influential variables and evaluates their relative importance. A case study based on Seattle's data is used to verify the proposed framework, and some conclusions can be drawn: (1) Urban morphology and building geometry have significant effects on evaluating the building energy consumption and greenhouse gas emissions, as the accuracy of predicted result improve 33.46% compared with only considering building characteristics; (2) The total gross floor area and natural gas are identified as the most influential factors for energy consumption and GHG emissions, respectively; (3) The proposed model is examined to be an accurate method with the R2 of 0.8435 on average, comparing with the other approaches, such as the eXtreme Gradient Boosting, Random Forest, and Support Vector Regression. The main contributions of this research lie in that (a) a comprehensive structure integrated with building characteristics, building geometry, and urban morphology is established to forecast the energy use and greenhouse gas emissions; (b) an explainable artificial intelligence model incorporated with the SHapley Additive exPlanation algorithm into Light Gradient Boosting Machine has been proved to achieve an accurate prediction of the energy performance of residential buildings.