Performance Of Residential Buildings Research Articles

Optimizing the thermal performance of residential buildings in Jordan

This work investigates the influence of building form, natural ventilation, and window shading passive strategies on the thermal performance and thermal comfort of three residential building forms, namely rectangular, L-shape, and U-shape, in the hot-dry summer in Amman, Jordan. The thermal performance of buildings is studied using indoor air temperature, and the thermal comfort is evaluated according to ASHRAE-55 and Fanger’s models. The buildings’ models are designed using the SketchUp plugin OpenStudio and then plugged into the EnergyPlus simulator. It is obtained that when natural ventilation and shading devices are considered together, the indoor air temperature is notably dropped up to 8.85 °C in the daytime and 12.70 °C at nighttime, which improves the thermal comfort of all building forms. Generally, rectangular building exhibits the best thermal behavior under optimum conditions. U-shape presents the best thermal behavior for the primary conditions, and it records the lowest out-of-comfort percentage time during the day and achieves the highest comfortable percentage after considering the window’s area control.

Evaluation of heating load energy performance in residential buildings through five nature-inspired optimization algorithms

This study evaluates a new hybrid approach for determining homes’ heating load (HL). The crow search algorithm (CSA), heap-based optimizer (HBO), seeker optimization algorithm (SOA), political optimizer (PO), and harmony search (HS) are the five components of the suggested paradigm. A nonlinear analysis of the effects of eight independent factors on the HL was conducted using the best structure identified in each model. The assessment procedure for the HS technique in this study consisted of three parts. The appropriate population size to utilize in the first phase was found to be the one that yields the best coefficient of determination (R2) value and the lowest root mean squared error (RMSE) value. For CSA-MLP, HBO-MLP, SOA-MLP, PO-MLP, and HS-MLP, respectively, the first phase yielded R2 = 0.96473, 0.95618, 0.96931, 0.97048, and 0.96702, and RMSE = 2.57119, 2.85968, 2.40125, 2.3554, and 2.46872. A battery of tests using a range of different nNew values (between 10 and 100) was applied to the HS-MLP with a population size of 50 in the second phase. The data indicates that the most reliable results are obtained with a nNew-value of 60. For training and testing, this value has RMSE values of 2.61518 and 2.4387 and R2 values of 0.9669 and 0.96783. In the third stage, an experiment with a population size of 50 and nNew of 60 was examined using a range of HMCR values (between 0.5 and 1.4). Concerning training and testing, the results indicate that the HMCR value 1.1 produces the most reliable results; its R2 values are 0.9739 and 0.97207, and its RMSE values are 2.32691 and 2.27488. Lastly, the results demonstrate that the accuracy of the HS-MLP method has been improved by the 3-phase analysis method.

Net zero energy analysis and energy conversion of sustainable residential building in Muscat, Oman

The building sector is the predominant consumer of primary energy globally. The building sector accounts for around 40% of global energy production.Net Zero Energy Buildings (NZEBs) are highly suggested by energy experts as an effective option to alleviate the strain on primary energy sources caused by the building sector. The disparity between energy performance predictions provided during the design phase and the actual energy performance of residential buildings is mostly attributed to a limited comprehension of the components that influence energy consumption and the constraints of whole building simulation software. The objective of this research was to perform a comparison analysis of the expected and actual energy consumption of a prototype net-zero energy house built at the University of Technology and Applied Sciences in Muscat. The Hourly Analysis Programme (HAP V4.2) was utilised to forecast the energy consumption of a Net Zero Energy Building (NZEB) at HCT, taking into account the availability of an Energy Recovery Ventilator (ERV) and the absence of an ERV. The newly built house underwent a one-month testing phase to fulfil many duties according to competition regulations. One of the main goals was to generate on-site energy through photovoltaic panels, producing an amount proportional to the energy consumed by the house. Upon comparing the actual energy consumption data with the simulated result, it was noticed that the actual energy demand of the house was around 20% lower than the prediction made by the simulation tool.

Energy Performance in Residential Buildings as a Property Market Efficiency Driver

Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents’ well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis.

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.

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.

Assessment of the Daylight Performance in Residential Buildings: the Impact of Setback Regulations in the Jordanian Building Code

Assessment of the Daylight Performance in Residential Buildings: the Impact of Setback Regulations in the Jordanian Building Code

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.

Predicting climate change and occupants’ behaviour impact on thermal-energy performance of global south housing: Case study in Brazil

Due to climate change conditions, natural ventilation potential may reduce over the years and increase dependence on HVAC systems. Moreover, occupants’ behaviour regarding natural ventilation is a significant parameter affecting the thermal-energy performance of residential buildings as people tend to occupy their homes differently depending on their life, work and cultural routines. Therefore, in this study, the thermal-energy performance of a Global South (GS) housing case study located in Brazil was assessed in a future weather context. This paper included two major steps: (1) Optimization procedure to create optimized models based on different occupancy patterns; and (2) Parametric analysis to explore the building’s thermal-energy performance for a given constructive design option, occupant behaviour and weather data. The optimization procedure included a multi-objective optimization based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to minimize discomfort hours and cooling energy demand, while parametric analysis explored the occupants’ behaviour varieties derived from alternative occupancy patterns, ventilation availabilities and HVAC operation modes. The obtained future context simulation results indicated an increase in discomfort hours and cooling energy demand, while the most appropriate architecture design might vary depending on the occupancy behaviour.

Data-driven modelling of building retrofitting with incomplete physics: A generative design and machine learning approach

Building performance simulation (BPS) based on physical models is a popular method for estimating the expected energy savings from energy-efficient building retrofitting. However, for many buildings, especially older buildings, built several decades ago, an operator do not have full access to the complete information for the BPS method. Incomplete information comes from the lack of detailed building physics, such as the thermal transmittance of some building components due to the deterioration of components over time. To address this challenge, this paper proposed a data-driven approach to support the decision-making of building retrofitting selections under incomplete information conditions. The data-driven approach integrates the backpropagation neural networks (BRBNN), fuzzy C-means clustering (FCM), and generative design (GD). It generates the required big database of building performance through generative design, which can overcome the problem of incomplete information during building performance simulation and energy-efficient retrofitting. The case study is based on old residential buildings in severe cold regions of China, using the proposed approach to predict energy-efficient retrofitting performance. The results indicated that the proposed approach can model the performance of residential buildings with more than 90% confidence, and show the variation of results. The core contribution of the proposed approach is to provide a way of performance prediction of individual buildings resulting from different retrofitting measures under the incomplete physics condition.

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

Mechanical Ventilation Heat Recovery Modelling for AccuRate Home—A Benchmark Tool for Whole House Energy Rating in Australia

To manage energy-efficient indoor air quality, mechanical ventilation with a heat recovery system provides an effective measure to remove extra moisture and air contaminants, especially in bathrooms. Previous studies reveal that heat recovery technology can reduce energy consumption, and its calculation needs detailed information on the thermal performance of exhaust air. However, there are few studies on the thermal performance of bathroom exhaust air during and after showers. This study proposed a detailed thermal performance prediction model for bathroom exhaust air based on the coupled heat and mass transfer theory. The proposed model was implemented into the AccuRate Home engine to estimate the thermal performance of residential buildings with heat recovery systems. The time variation of the water film temperature and thickness on the bathroom floor can be estimated by the proposed model, which is helpful in determining whether the water has completely evaporated. Simulation results show that changing the airflow rate in the bathroom has little effect on drying the wet floor without additional heating. The additional air heater installed in the bathroom can improve floor water evaporation efficiency by 24.7% under an airflow rate of 507.6 m3/h. It also demonstrates that heat recovery can significantly decrease the building energy demand with the fresh air load increasing and contribute about 0.6 stars improvement for the houses in Hobart (heating-dominated region). It may be reduced by around 3.3 MJ/(m2·year) for the houses in other regions. With this study, guidelines for optimizing the control strategy of the dehumidification process are put forward.

EXPLORING BIOCLIMATISM IN THE POST-OCCUPANCY EVALUATION OF HOUSEHOLDS

Introduction: Housing complexes represent a significant part of the built environment in urban areas, playing a crucial role in providing housing for increasingly dense populations. However, the effectiveness of these projects in providing healthy and sustainable living environments is often questioned after occupation. Thus, this research proposes to explore the relevance of bioclimatism in the post-occupation evaluation of housing complexes. By examining this intersection between bioclimatism and post-occupation, we can identify gaps in the design, construction and management of housing complexes, thus contributing to the evolution of architectural and urban practices. Objective: To understand how bioclimatic principles influence the performance of residential buildings after occupation. Methodology: This study relies on a literature search approach to explore the role of bioclimatism in the post-occupancy assessment of housing developments. Results and Discussion: The results obtained from the post-occupancy analysis of housing complexes with a focus on bioclimatism revealed significant insights into the influence of these approaches on the performance of buildings and the well-being of occupants. The selected case studies spanned a variety of climate contexts and architectural designs, allowing for a comprehensive understanding of the impacts of bioclimatism in practice. A striking finding is that bioclimatic principles play a decisive role in the thermal comfort of living spaces. Buildings that incorporated design strategies aimed at solar orientation, cross ventilation and the use of shading elements demonstrated superior performance in maintaining adequate indoor temperatures. Conclusion: The bioclimatic approach not only improves the performance of buildings, but also creates healthier, more sustainable and more pleasant living environments. However, to maximize the benefits of bioclimatism, an ongoing effort in awareness, education and collaboration between professionals and occupants is required. The development of specific guidelines for different climate contexts and the promotion of sustainable building standards can further enhance the positive impact of bioclimatism on urban housing.

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.

Impact assessment of climate change on energy performance and thermal load of residential buildings in New Zealand

While it is evident that climate change will have an impact on the energy demand for heating and cooling in buildings, the exact extent of this impact is not yet fully understood. Quantification of future cooling and heating need in buildings provides a basis for taking appropriate measures for building climate change adaptation. The focus of this study is to examine how future climate change scenarios will impact the heating and cooling of residential buildings across different climatic regions in New Zealand. The future weather data under changing climate were generated for six climatic zones of New Zealand employing the statistical downscaling method. The study used various climate change scenarios, which represent concentration pathways (RCPs), to generate weather data. Specifically, the RCP8.5 and RCP4.5 scenarios were employed in the building performance simulations for different prototypes of residential buildings. The results showed there would be a significant change in the thermal performance of residential buildings, with a noticeable increase in cooling load and a decrease in heating load. These changes include a maximum thermal load change of 3 kWh/m2 in Auckland by 2090, 2.7 kWh/m2 in Hamilton, 8.3 kWh/m2 in Wellington, 4.2 kWh/m2 in Rotorua, 11 kWh/m2 in Christchurch, and 11.6 kWh/m2 in Queenstown. The warmer climatic zones are expected to change from a heating dominated to a cooling-dominated zone. The results indicated the importance of considering present and future climatic conditions in design and establishing a foundation for actions for the resilience of buildings to climate change.

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.

Evaluation of Multi-Functional Variable Refrigerant Flow System With Thermal Energy Storage and Photovoltaic-Based Distributed System for Net-Zero Energy Home Design

Abstract Efficient heating and cooling systems and renewable energy sources are crucial for effectively designing net-zero energy homes (NZEHs). The study proposes using a multi-functional variable refrigerant flow system with hydraulic heat recovery (MFVRF-H2R) to reduce heating, ventilating, and air-conditioning (HVAC) and hot water energy usage, offering a practical approach to enable NZEH solutions. Photovoltaic (PV)-based on-site power generation is utilized to achieve zero energy performance in residential buildings. A building energy simulation study is conducted to assess the effectiveness of the combined systems in various climate conditions. To develop the simulation model, the US National Institute of Standards and Technology (NIST)’s net-zero energy residential test facility is used as the benchmark for NZEH baseline models. The MFVRF-H2R system is incorporated into the NZEH baseline to propose a more-energy-efficient design with heat recovery technology. eQUEST and post-processing calculations are used to simulate NZEH performance, comparing whole-building energy end-use and PV capacity for the baseline and alternative models with MFVRF-H2R. Results demonstrate that the proposed variable refrigerant flow (VRF)-based NZEH design can provide potential energy savings of up to 32% for cooling energy under various climate zones. Moreover, the NZEH design with the proposed MFVRF-H2R can achieve up to a 90% reduction in domestic hot water usage compared to an NZEH design without VRF heat recovery technology. The study suggests that the MFVRF-H2R system can provide practical and realistic solutions for making HVAC energy-efficient by minimizing thermal waste and reusing it for other thermal parts of the building, such as hot water applications. Consequently, this study highlights the effectiveness of the MFVRF-H2R system in designing NZEHs while considering heat recovery and renewable energy technologies.