Energy Efficiency Of Residential Buildings Research Articles

Dynamic Indoor Environmental Quality Assessment in Residential Buildings: Real-Time Monitoring of Comfort Parameters Using LoRaWAN

This study addresses an identified literature gap regarding indoor environmental quality in residential buildings, where the primary focus has traditionally been on energy performance rather than comfort optimization. Leveraging the low-cost and easy-to-implement LoRaWAN protocol, this research collects and analyses real-time data on comfort parameters, including temperature, CO2 levels, humidity, lighting, atmospheric pressure, and total volatile organic compounds (TVOC) across various buildings within the INCUBE EU project. The results highlight the dynamic nature of the parameters and emphasize the importance of continuous monitoring to enhance comfort and energy efficiency in smart residential buildings. The findings advocate for integrating technologies like LoRaWAN to optimize indoor environmental quality, ultimately improving residential comfort and occupant well-being.

China's newest design of apartment buildings with modernized façade: A comparative evaluation of its energy performance in five major climate zones

Many studies have indicated that the energy efficiency of residential buildings can greatly mitigate energy shortages and environmental problems. High window-to-wall ratio, flat geometric shape, and open space layout are the key characteristics of apartment buildings with modernized façade (ABMF), and the major design trend dominating China's housing market. This study aims to understand the isolated effects of these three design factors on the energy performance of ABMF in five major climate zones using a parametric performance design method. Three important findings were concluded: 1) the energy use intensity (EUI) of ABMF showed a positive relationship to the window-to-wall ratio in five climate zones. Qingdao in Cold Region (2.12 kWh/m2) and Kunming in Temperate Region (1.04 kWh/m2) exhibited lowest average increase in EUI; 2) the reduction of EUI was twice as much in Kunming than in the other four cities, when the aspect ratio of ABMF changed from 1.75 to 0.95; 3) the ‘exterior-side hall’ was the most energy-efficient layout in the five cities. This study is the first to investigate the energy performance of the newest apartment design and hopefully can attract more studies to explore the suitability of ABMF for the sustainable development of China's housing market.

Urban heat islands’ effects on the thermo-energy performance of buildings according to their socio-economic factors

Urban areas experience the urban heat island (UHI) effect, which affects the thermal comfort and energy consumption of buildings. These consequences could vary depending on the socio-economic status of the neighbourhoods. Few studies have investigated how UHI affects socio-economically contrasting districts in thermal comfort and energy performance. Therefore, the primary goal of this research is to evaluate and compare the energy efficiency and thermal comfort conditions of residential buildings in the same city (Temuco, Chile) but located in socio-economically contrasting neighbourhoods. Urban weather files were first modelled in four urban zones using UWG software. Also, EnergyPlus building simulations were conducted to evaluate discomfort hours in adaptive comfort models and energy performance. The results showed annual average UHI intensities between 1.5 and 2.5 K. Urban–rural cooling energy load differences ranged between 12.47% and 38.92%, while heating energy load differences ranged between -20.47% and -81.95%. These distinctions depended on the urban zone, residence model analysed, or energy building standard applied. Similarly, urban-rural differences in thermal comfort times varied from 0.5% to 100%. Results illustrate that the risk of overheating could increase in socio-economically vulnerable areas. This issue could worsen if urban segregation continues to generate poor urban design in low-income districts.

Development and validation of the motivation for electricity saving behaviour scale (MESBS) among residents living in energy-efficient buildings in Sweden

Even though energy-efficient technologies are well developed, the behaviour of building occupants plays an important role in the search for the optimum level of energy efficiency. In academic literature, however, very few studies have used psychological theories to understand individuals’ drivers of energy use behaviour when interacting with energy-efficient technologies. Specifically, there are no studies solely focused on individuals' motivations for electricity saving behaviour. This paper is based on an empirical study carried out in energy-efficient residential buildings to develop and test a new scale by applying self-determination theory (SDT). A postal survey about the Motivation for Electricity Saving Behaviour Scale (MESBS), including self-reported electricity use, was sent out to 1,084 residents living in newly built energy-efficient multifamily dwellings and eco-villages in Southern Sweden. The answers obtained from 235 participants were analysed for the reliability and validity of MESBS. The findings indicated that i) MESBS items have good internal consistency and are in line with expectations based on SDT, and ii) the autonomous motivation component of MESBS is strongly associated with electricity saving behaviour (i.e. turning off lights when not needed) in energy-efficient buildings. Our results emphasise the importance of considering individuals’ motivations for undertaking electricity saving behaviour in energy-efficient buildings. The study also provides useful information for future studies of energy-efficient buildings as well as designing new technologies and interventions to reach the optimum level of energy-efficiency.

BIM-based energy consumption analysis for residential buildings: effects of orientation, size, and type of windows

Buildings have a significant environmental impact due to their reliance on nonrenewable energy sources. To address this concern, computer software is utilized to estimate building energy requirements. Building Information Modeling (BIM) has emerged as a valuable tool in this process, with BIM 6D offering a particularly beneficial feature. BIM 6D provides a comprehensive energy model of the building, simulating its actual energy performance. By analyzing natural and artificial lighting systems, particularly daylighting, BIM 6D can assist in optimizing energy efficiency. Despite being underutilized, the potential of this feature to enhance building sustainability is noteworthy. BIM 6D facilitates well-informed design and operational decisions for both new constructions and the renovation of existing buildings. The study examines design criteria such as building orientation, window orientation, size, and type to optimize energy consumption in a bungalow in the Sangli region of Maharashtra, underscoring the importance of energy-efficient buildings to local communities. Energy analysis and optimization are conducted using the Autodesk Insight tool. The methodology involves creating 3D models with Autodesk Revit Architecture software and performing various analyses, including Building Energy Modeling (BEM) simulations, Illuminance Analysis, Solar Access, and Daylight Autonomy assessments. The analysis findings indicate that low-e glass windows of small size and oriented between 0° and 90° significantly enhance energy efficiency under certain conditions, while changing the window type does not affect the analysis. The paper concludes with recommendations for window orientation and size to enhance energy performance in residential buildings, contributing to the broader goal of sustainable and energy-efficient construction practices. This study provides valuable insights for designing energy-efficient residential buildings in rural areas, addressing awareness gaps, and promoting sustainable construction practices.

The impact of urban form on the energy efficiency of residential buildings in a hot climate. Case study: Ouargla city

Urban development is intensifying significantly in cities, mainly due to rapid population growth. However, this development is accompanied by increased vulnerability to climate change, leading to higher energy consumption. This energy demand is even more pronounced in hot climates, where temperatures are very high during the summer season. The Algerian Sahara is no exception, and is experiencing very accelerated development, which has given rise to the appearance of new urban districts, and different urban characteristics compared to the original core, which constitutes the urban form most adapted to the desert climate and in perfect harmony with the immediate environment. This study attempts to assess the effect of the different urban forms that make up the fabric of the city of Ouargla in south-east Algeria, taken as a case study, on the energy consumption of buildings in a hot climate. This research adopts a mixed approach to evaluate the energy consumption of three buildings located in three neighbourhoods with different urban characteristics. A quantitative estimate of the energy consumed for cooling during the summer season for the three typical buildings selected was simulated using DesignBuilder software, taking into account the city’s specific climatic data, as well as the urban characteristics of the three neighbourhoods selected for the study. As a result, this evaluation covers both the architectural and urban scales. For the qualitative assessment, a field survey was carried out using questionnaires to assess the quality of thermal comfort outside neighbourhoods and its impact on building interiors, and consequently on energy consumption. The results indicate the significant effect of urban form on energy consumption, confirming that the traditional form is the most appropriate to achieve better energy efficiency in a hot climate. Therefore, the research recommends taking into account various influential factors and applying design strategies, regarding urban form, from the first stage of the design process.

Synergistic Neural Network and Velocity Pausing Particle Swarm Optimization for Enhanced Residential Building Energy Efficiency: A Case Study in Kuwait

The global energy demand of buildings is on the rise, driven by factors such as rapid population growth, increasing comfort, technological advances, and ongoing developments in building construction. This escalating energy consumption in buildings is a major contributor to the energy crisis and climate change. Accurate prediction of building energy consumption is essential for gaining insight into energy utilization, reducing waste, and enhancing comfort conditions. This study aimed to introduce a reliable technique for predicting and optimizing the energy consumption of residential buildings, with a focus on a case study in Kuwait. A robust Artificial Neural Network (ANN) was developed, meticulously trained, and rigorously tested to provide accurate energy consumption predictions. Subsequently, an innovative variant of the Velocity Pausing Particle Swarm Optimization (VPPSO) algorithm was employed to identify optimal energy consumption solutions. This novel optimization technique can achieve significant reductions in building energy consumption, with potential savings of up to 43%. Additionally, a sensitivity analysis was performed using the Garson method to assess the impact of input parameters on energy utilization. The results reveal that the insulation and cooling setpoint exert the greatest influence on the objective function, followed by the outdoor airflow. The proposed model, which combines the power of ANN with VPPSO, can be applied to similar buildings, offering precise predictions and optimizing energy consumption. This approach holds promise in addressing the pressing challenges of energy efficiency in building environments.

Optimizing energy efficiency in mediterranean single-family homes: A parametric study of building typology, orientation, and BIPV integration

The energy efficiency of residential buildings is a critical concern, especially in regions with challenging climatic conditions like the Mediterranean. Despite numerous studies on energy-efficient design, there is a lack of specificity in how building typology, orientation, and Building-Integrated Photovoltaics (BIPVs) interact to influence energy performance. This study addresses this gap by conducting a parametric investigation of single-family houses, focusing on variations in building typology and orientation, both with and without BIPV integration. The parameters evaluated include energy consumption for heating, cooling, lighting, and domestic hot water, using simulations performed with iSBEM-Cy, the official tool for energy performance calculations in Cyprus. The results reveal that building orientation can reduce energy consumption, while BIPV integration contributes additional energy savings. Terraced houses with only Building-Applied Photovoltaics (BAPVs) showed the best energy balance (−38.20 kWh/m2/yr), while detached houses had the highest energy consumption (71.24 kWh/m2/yr). With BIPVs, energy balances improved significantly across all typologies, with terraced houses reaching −78.20 kWh/m2/yr, demonstrating the strong potential of BIPV integration. These findings highlight the importance of considering context-specific factors when integrating renewable energy systems, offering insights for architects and urban planners.

Prediction of Energy Efficiency for Residential Buildings Using Supervised Machine Learning Algorithms

Prediction of Energy Efficiency for Residential Buildings Using Supervised Machine Learning Algorithms

Parameters and barriers affecting the marketability of energy efficient residential buildings: a case study in Turkey

ABSTRACT The data indicate that merely improving energy efficiency within energy-efficient buildings (EEBs) is insufficient to significantly reduce the energy demand of the building sector. To achieve substantial reductions, strategies must also focus on increasing the number of EEBs within the existing building stock. However, research in this area remains limited. Therefore, this study aims to explore the current market demand and barriers for EEBs, examine the impact of various design decisions on housing prices, identify the factors influencing the marketability of residential buildings, and analyze customer purchasing decisions in Turkey (Türkiye). For this purpose, a comprehensive literature review was first made. It was observed that factors such as macroeconomic conditions, sales price, housing opportunities, location, brand, customer demographics, energy efficiency, housing features, and aesthetics were frequently discussed in the literature. Based on the literature review and preliminary study results, among other factors, aesthetic appreciation is considered the most applicable design strategy, and windows and balconies are identified as building components that can simultaneously affect energy efficiency, marketability, and aesthetics. Then, as a preliminary study to determine major marketing strategies, the housing marketing strategies were surveyed and analyzed from the real estate sector in different cities as provided in the advertisements posted on their websites. Furthermore, a comprehensive e-mail survey was conducted with 351 different real estate companies amongst 22 cities of Türkiye. Based on the findings, enhancing architectural designs could potentially elevate the perceived value of energy-efficient homes by up to 18% among consumers and this can increase the marketability.

Full-Scale Comparison of Two Envelope Systems for Lightweight Wooden Framing in Cold Climates

Residential homes and apartments’ cooling and heating needs account for 63% of total building energy consumption. Improvements in the properties of building envelopes are among the best ways to reduce their energy consumption. The project’s general objective was to compare the performance of externally insulated and traditional envelopes of light wooden frame buildings at full scale. Two houses were constructed and equipped with relative humidity sensors and temperature probes to assess the physical properties of the building envelope. The first house was built according to the conventional method (insulation between the studs), and the second house was built according to the method with the insulation outside the wall (also known as the perfect wall). The results showed that external insulation effectively mitigates internal condensation risks by relocating dew points to the exterior surface, thereby enhancing structural durability and thermal stability. Thermographic imaging confirmed reduced thermal bridging and improved thermal performance in the externally insulated walls. Overall, this study supports, with a full-scale experiment, the adoption of external insulation as a viable strategy for enhancing energy efficiency, thermal comfort, and durability in residential buildings.

Research of electrode materials for the creation of multifunctional current sources with increased capacity as a components of the energy sector of an efficient urban environment

As part of creating a comfortable and safe environment, constructing energy-efficient residential and industrial buildings and structures that meet modern requirements and standards, the development of the production of environmentally friendly renewable and new individual energy sources is becoming especially relevant. In this regard, there is a need to increase the energy capacity of electrochemical cells. Research has been carried out on the metallized conductive materials creation based on rolled carbon non-woven material “Busofit” with the sequential application of metal coatings of titanium and silver using ion-plasma sputtering and electric pulse dispersion methods. It has been shown that surface layer metallization of the electrode material with titanium can improve the electrochemical cell characteristics. Additional silver film deposition leads to further cell performance improvement. It has been confirmed that the multilayer structure interfacial resistance between the carbon and the current collector has a significant effect on the conductivity of the electrochemical cell and the stability of its operation. The contact area increase of the electrode with the electrolyte leads to an increase in the rate processes occurring on the electrode surface and in the near-electrode space, which opens up prospects for increasing the energy intensity of the electrochemical system. A significant capacity increase of a water-based capacitor structure is achieved by the formation of a nanostructured dielectric layer of potassium titanate in the interelectrode space. It has been confirmed that the cell voltage cycling helps to stabilize the processes occurring in the surface layer of the electrode material at the interface and determining the range of mechanisms for transmitting electrical energy, which makes it possible to achieve higher energy intensity of the samples. Improvement of technological solutions in the field of ion-plasma technologies and the use of new perspective nanostructured materials creates the prerequisites for the creation of advanced automation and energy supply systems with a higher resource, which expands the possibilities of their use in various construction projects.

Optimal HVAC setpoints for energy efficiency and thermal comfort in Chinese residential buildings

The easiest way to improve energy efficiency and thermal comfort in existing buildings is to adjust setpoints. However, although Chinese standards regulated the heating and cooling setpoints for different climate zones in China, they lacked consideration for occupants’ thermal comfort and ignored the ventilation setpoints. Therefore, this study aimed to identify optimal heating, cooling, and ventilation setpoints at the design stage to enhance building energy performance and indoor thermal comfort in Chinese residential buildings. This study used DesignBuilder to simulate the energy usage and thermal comfort with different setpoints and judged which combination was the best. To conduct this study across the whole of China, the typical cities in different climate zones were selected by using machine learning. Results indicated that adjusting setpoints in Harbin, Beijing, and Shanghai could increase comfortable days by 43.95 %, 54.23 %, and 23.36 % respectively, with minimal impact on energy usage. In Guangzhou, energy usage could be reduced by 7.56 % with a 5.91 % decrease in comfortable days, while Kunming could see an 11.11 % increase in comfortable days with a 5.92 % rise in energy consumption. Additionally, sensitivity analysis revealed that activity template variation was most critical for energy usage in Harbin, Beijing and equipment power density variation was most important for thermal comfort in Shanghai, Guangzhou, and Kunming. Furthermore, proposed setpoint combinations demonstrated resilience to future weather conditions in most typical cities, except for Guangzhou. These findings provide valuable insights for designers and researchers aiming to retrofit residential buildings, emphasizing the importance of considering both energy efficiency and occupant comfort. This study introduces innovative approaches like comprehensive setpoint optimization and machine learning for typical city selection, providing practical solutions to improve energy efficiency and thermal comfort in residential buildings.

Instruments for Supporting Implementation of Sustainable Development Goals for Construction Sector – Case of Poland

Currently, three billion people face dangerous levels of air pollution and rely on non-renewable energy sources for cooking and heating. Due to its environmental impact, the construction sector is a particularly important area for implementing sustainable development policies. On July 10, 2023, the UN published The Sustainable Development Goals Report 2023 (SDGs). In accordance with the provisions of Goal 7 (which was discussed in the above document), the international community should increase its share of renewable energy sources in the global energy mix and promote investment in clean energy technologies by 2030. Achieving this goal necessitates the introduction of support mechanisms to enhance the energy efficiency of residential buildings. Hence, this study aims to showcase the legal and financial tools that aid the growth of energy-efficient construction in Poland, to compare the renovation costs of an existing residential building in order to meet energy-efficiency standards, and to detail the financial support that is available for planned thermal-modernization efforts.

Empowering society and culinary MSMEs for sustainable development goals initiatives in West Java

Purpose: This study aimed to explore the impact of empowering society and culinary MSMEs on Sustainable Development Goals (SDGs) in West Java, focusing on Sindangmukti Village, Karawang Regency. While prior research has often used a top-down approach, neglecting direct societal impact, this study takes a bottom-up perspective, aligning with implicit SDG principles observed in community services. Research Methodology: The study employed online questionnaires with 505 households in West Java to evaluate water consumption, waste generation, and energy usage patterns. Specific investigations in Sindangmukti Village have measured community service effectiveness regarding SDGs. Results: The study found that Only 19.1% of the participants set AC temperatures within the recommended ranges. Additionally, initiatives to promote a sustainable city strategy and establish energy-efficient culinary MSMEs in Sindangmukti Village resulted in a 29% increase in knowledge among 70 participants and generated $36.71 income from bitter melon chip production within two months. Limitations: However, the study focused solely on Sindangmukti Village, neglecting the diverse cultural and problem landscapes of other villages in the Karawang Regency. Contributions: The findings of this study can inform future efforts to enhance energy, water, and waste efficiency in residential buildings in West Java, thereby fostering awareness of energy conservation, environmental sustainability, and economic resilience.

Prediction of residential building occupancy using Machine learning with integrated sensor and survey Data: Insights from a living lab in Morocco

Building occupancy information is essential for effective energy management in buildings through the adoption of energy conservation and occupant-centric control strategies. These strategies endeavor to contribute to optimizing energy consumption while ensuring occupant comfort. This study focuses on advanced occupancy modeling techniques to enhance energy efficiency in residential buildings, utilizing various data-driven techniques. Various Machine Learning models, such as Random Forest, Bayesian Network, Decision Trees, Support Vector Machines, K-Nearest Neighbors, eXtreme Gradient Boosting, and Regularized Greedy Forest, have been evaluated for predicting and classifying building occupancy. Employing a living lab approach within a residential setting, the research evaluates model performance using two ground truth datasets: IoT sensor and survey data. Experimental tests use Accuracy and F1_score as evaluation criteria, demonstrating accuracy rates ranging from 70% to 95.96%. The results highlight the performance of these models in predicting residential occupancy, offering valuable insight into two approaches to occupancy modeling. The Random Forest model performs exceptionally well in capturing occupancy trends, while the Bayesian Network model, combined with expert knowledge, provides detailed predictions of occupancy types and zones. The research also addresses challenges related to data collection, including privacy concerns. It presents effective occupancy modeling strategies for residential energy management.

Utility-based context-aware multi-agent recommendation system for energy efficiency in residential buildings

A significant part of CO2 emissions is due to high electricity consumption in residential buildings. Using load shifting can help to improve the households’ energy efficiency. To nudge changes in energy consumption behavior, simple but powerful architectures are vital. This paper presents a novel algorithm of a recommendation system generating device usage recommendations and suggests a framework for evaluating its performance by analyzing potential energy cost savings. As a utility-based recommender system, it models user preferences depending on habitual device usage patterns, user availability, and device usage costs. As a context-aware system, it requires an external hourly electricity price signal and appliance-level energy consumption data. Due to a multi-agent architecture, it allows for easy integration of new agents, enabling seamless functionality expansion, or the disabling of existing agents to tailor the system to specific needs. Empirical results show that the system can provide energy cost savings of 18% and more for most studied households.

Deep learning–based urban energy forecasting model for residential building energy efficiency

Sustainable and inventive city design is becoming more and more dependent on the use of cutting-edge technology as smart cities develop further. Energy efficiency optimization in residential structures is an essential part of the puzzle as it helps conserve resources and keeps the planet habitable. An enhanced Deep Neural Network (DNN) model for household energy efficiency predictions is presented in this research. Our model uses a large dataset of building features, weather, occupancy patterns and energy usage histories. Data is preprocessed, features are engineered and hyperparameters are tweaked to improve DNN prediction. Scalable, easy-to-understand models are essential, as are shifting urban areas and energy landscapes. In this work, the authors have evaluated the proposed model with basic model with different optimizers. Initially, the Stochastic Gradient Descent optimizer applied that gained 91.02% Recall, 93.47% Precision, 93.28% F1-Score, 0.0153 MSE, 0.0166 RMSE and 0.0165 MAE. The proposed model gained 99.52% Recall, 98.91% Precision, 99.09% F1-Score, 0.0140 MSE, 0.0137 RMSE and 0.0139 MAE. By monitoring, analyzing and making decisions in real time, smart city systems can help planners understand energy usage trends. The optimized DNN model advances smart city development by promoting sustainability and resource optimization. Predicting residential buildings’ energy efficiency provides proactive energy savings, cost reduction and environmental impact mitigation. The suggested DNN model shows how smart cities use cutting-edge urban planning to become more sustainable, efficient and resilient.

Assessing Residential Building Energy Efficiency Using Evolutionary Dendritic Neural Regression

Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain.

Effectiveness of building envelope parameters and adopting PV panels to reduce reliance on local generators in hot-dry climate

The growing energy demand, associated with the inability of the current infrastructure to satisfy this demand, has presented numerous challenges in Iraq's electricity sector. As a result, there has been an increased dependence on local diesel generators to mitigate power outages in homes. However, these generators raise environmental concerns and are associated with high operating CO₂ emissions. Here, using the DesignBuilder and EnergyPlus simulation software, the effectiveness of different building envelope modifications and photovoltaic panels as alternative energy sources was examined. Specifically, the impact of wall and roof insulation, window glazing, and shading devices on energy efficiency was analyzed. The results indicated that roof insulation is the most effective in reducing energy consumption by 28.8%, followed by wall insulation by 13.01%, while the effect of windows glazing and shading devices was insignificant. Furthermore, the installation of solar panels led to a significant reduction in energy demand by 53.6%, thereby decreasing operating carbon dioxide emissions and providing a practical alternative to the use of local generators. Our study offers valuable insights into the design of energy-efficient residential buildings in hot and dry climates. It highlights the importance of selecting appropriate building materials and integrating renewable energy sources, presenting a more environmentally effective solution to mitigate energy shortages.