Residential Buildings Research Articles

Development of PCM tile for residential buildings in hot and dry climate: design and optimization

In recent years, the building sector has become more conscious of sustainability, and the use of phase change materials (PCMs) in concrete has gained more attraction. Integration of PCM with building facades is a successful method to reduce energy consumption and improve human comfort. However, no single PCM can work in an all-weather scenario. Hence, in this research work, an attempt is made to select a suitable PCM for Chennai city, India, and this methodology can be employed at any geographical location. The PCM tile is designed by including certain features to increase the thermal conductivity of the PCM zone. The optimum design of the PCM tile is achieved through multi-objective optimization techniques. L27 orthogonal array is employed, and all the tests were conducted through validated numerical simulation. Redesigned PCM tile includes a PCM layer of 2 cm thickness with a 10% mix of copper nanoparticles covered by plaster. Redesigned PCM tile reduces the peak indoor temperature by 6.62℃ compared with conventional roof systems.

Pathways to a net zero building sector in Colombia: Insights from a circular economy perspective

The rapid urbanization and economic growth in Colombia have made the residential sector one of the largest energy and material consumers in the country. Circular economy (CE) strategies are a promising alternative to decouple the sector from resource depletion and climate change. The analysis shown here is based on the RECC model framework, a dynamic material flow analysis (dMFA) model. Our results suggest that by 2050 without CE strategies, residential building stock will reach 2.5 billion m2 and emit between 13 and 25 Mt CO2/yr, depending on whether carbon forest sequestration is included or not. A complete implementation of CE strategies and lower floor space can reduce 2020–2050 cumulative primary material production, waste generation, and GHG emissions by 48, 5, and 49 %, respectively. There is a potential to achieve net zero emissions by 2050 if, in addition to the measures mentioned above, more wood materials and forest sequestration are considered.

A Cooperative Multi-Agent Q-Learning Control Framework for Real-Time Energy Management in Energy Communities

Residential and commercial buildings are responsible for 35% of the EU energy-related greenhouse gas (GHG) emissions. Reducing their emissions is crucial for meeting the challenging EU objective of the agenda for becoming a net-zero continent by 2050. The diffusion and integration of distributed renewable energy sources (RESs) and energy storage systems (ESSs), as well as the creation of energy communities (ECs), have proven to be crucial aspects in reducing GHG emissions. In this context, this article proposes a multi-agent AI-based control framework to solve the EC’s energy management problem in the presence of distributed RESs and ESSs as well as considering a shared ESS. The objectives of the proposed control framework are to satisfy the EC members’ load demand to maximize self-consumption and to manage ESSs charging and discharging processes, to enforce cooperative behavior among the EC members by adopting fair and personalized strategies and to maximize EC members’ profits. The proposed control procedure is based on three sequential stages, each solved by a dedicated local RL agent exploiting the Q-Learning algorithm. To reduce the computational complexity of the proposed approach, specifically defined state aggregation criteria were defined to map the RL agents’ continuous state spaces into discrete state spaces of limited dimensions. During the training phase, the EC members’ profiles and the ESSs’ and RESs’ characteristics were randomly changed to allow the RL agents to learn the correct policy to follow in any given scenario. Simulations proved the effectiveness of the proposed approach for different costumers’ load demand profiles and different EC configurations. Indeed, the trained RL agents proved to be able to satisfy the EC members’ load demands to maximize self-consumption, to correctly use the distributed and shared ESSs, to charge them according to respective personalized criteria and to sell the energy surplus, prioritizing sales to the EC. The proposed control framework also proved to be a useful tool for understanding EC performance in different configurations and, thus, for properly dimensioning the EC elements.

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.

Research on Energy-Saving Transformation of Rural Residential Building Envelope Structures and Heating Modes in Northeast China

Rural areas in Northeast China present a large demand for heating energy in winter, but there are problems in such areas with poor thermal performance of building envelopes and poor indoor thermal comfort. In addition, coal-fired boilers are still widely used. China’s “Dual-Carbon Goals” and “Clean Heating” policy call for the creation of a green and comfortable living environment for rural residential buildings. This paper considers the impact of the improvement of the thermal performance of envelope structures on the initial investment of the transformation program and the rated power of the ASHP and proposes an energy-saving transformation method to replace traditional coal-fired boilers with the ASHP on the basis of the improvement of the thermal performance of envelope structures. By establishing a typical rural residential building model in Northeast China, this energy-saving method is simulated based on TRNSYS. The results show that the payback period of investment of the transformation method of “envelope structure + heating system” is not superior to that of the transformation method of only improving the thermal performance of the envelope structure, but it has advantages in the comprehensive life-cycle benefits and it has great advantages in improving the satisfaction of rural residents in the use of heating systems.

From roads to roofs: How urban and rural mobility influence building energy consumption

Understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

Eye-tracking investigation of emotional feedback to southern Hebei courtyard gates

ABSTRACT The attention of residents has always been drawn to the courtyard gates of rural residential buildings, this is the basis for evaluating the first impression of the countryside by first-time visitors. Using eye-tracking technology, this study measures the pupil diameter, fixation duration, and fixation point of subjects as objective physiological data, Then combined with the results of the subjective satisfaction questionnaire for a comprehensive analysis. The findings showed that, When the subjects evaluated their satisfaction with the courtyard gates, the proportion and color of the modeling elements led to the subjects’ emotional changes, and the subjects focused mainly on the central axis and the connection of various structures with raised areas. In order to elicit positive emotions in the subjects, village gate designs should therefore adhere to the standard specification’s requirements as well as the following: the width of the gate should be 0.8 times the height, the height should be 5 times the width of the gateposts, and the color should match the material’s color. To meet the emotional needs of the visitors, the final design strategy of the proportion and color of rural courtyard gates in southern Hebei Province is proposed.

Sustainable Spatial Features of Settlements along the Miao Frontier Wall and Miao Frontier Corridor Analyzed through Machine Learning Clustering

This study employed unsupervised machine learning clustering algorithms to systematically analyze the spatial layout characteristics of residential buildings in villages along the Miao Frontier Wall and Miao Frontier Corridor in Western Hunan. The results indicated significant differences between the two regions in terms of the number of building clusters, distribution patterns, and compactness. A comparative analysis of the K-means and DBSCAN algorithms revealed that K-means is more effective in uncovering the internal spatial layout characteristics of settlements. Further analysis showed that villages along the Miao Frontier Wall exhibited greater diversity and complexity, whereas those along the Miao Frontier Corridor demonstrated higher clustering efficiency and denser internal building distribution. These differences can be attributed to variations in historical functions, geographical environments, planning concepts, and social structures. This research uncovers the spatial layout patterns of traditional settlements and proposes a machine learning-based approach to cultural heritage preservation, providing a theoretical foundation for future heritage conservation and spatial optimization, thereby promoting the sustainable development and protection of traditional cultural heritage.

Long-Term Comparative Life Cycle Assessment, Cost, and Comfort Analysis of Heavyweight vs. Lightweight Construction Systems in a Mediterranean Climate

Massive construction systems have always characterized traditional architecture and are currently the most prevalent, straightforward, and cost-effective in many Mediterranean countries. However, in recent years, the construction industry has gradually shifted towards using lightweight, dry construction techniques. This study aims to assess the effects on energy consumption, comfort levels, and environmental sustainability resulting from the adoption of five high-performance construction systems in a multi-family residential building: (i) reinforced concrete structure with low-transmittance thermal block infill; (ii) reinforced concrete structure with light-clay bricks and outer thermal insulation; (iii) steel frame; (iv) cross-laminated timber (CLT); (v) timber-steel hybrid structure. To achieve this goal, a multidisciplinary approach was employed, including the analysis of thermal parameters, the evaluation of indoor comfort through the adaptive model and Fanger’s PMV, and the quantification of environmental and economic impacts through life cycle assessment and life cycle cost applied in a long-term analysis (ranging from 30 to 100 years). The results highlight that heavyweight construction systems are the most effective in terms of comfort, cost, and long-term environmental impact (100 years), while lightweight construction systems generally have higher construction costs, provide lower short-term environmental impacts (30 years), and offer intermediate comfort depending on the thermal mass.

Investigating the Heterogeneity Effects of Urban Morphology on Building Energy Consumption from a Spatio-Temporal Perspective Using Old Residential Buildings on a University Campus

The significant increase in building energy consumption poses a major challenge to environmental sustainability. In this process, urban morphology plays a pivotal role in shaping building energy consumption. However, its impact may exhibit latent heterogeneity due to differences in temporal resolution and spatial scales. For urban energy planning and energy consumption modeling, it is crucial to pinpoint when and where urban morphology parameters matter, an overlooked aspect in prior research. This study quantitatively explores this heterogeneity, utilizing a detailed dataset from old residential buildings within a university campus. Spatial lag models were employed for cross-modeling across various temporal and spatial dimensions. The results show that annual and seasonal spatial regression models perform best within a 150 m buffer zone. However, not all significant indicators fall within this range, suggesting that blindly applying the same range to all indicators may lead to inaccurate conclusions. Moreover, significant urban morphology indicators vary in quantity, category, and directionality. The green space ratio exhibits correlations with energy consumption in annual, summer, and winter periods within buffer zones of 150 m, 50~100 m, and 100 m, respectively. It notably displays a negative correlation with annual energy consumption but a positive correlation with winter energy consumption. To address this heterogeneity, this study proposes a three-tiered framework—macro-level project decomposition, establishing a key indicator library, and energy consumption comparisons, facilitating more targeted urban energy model and energy management decisions.

Assessing and mitigating building fire risk based on the scenario clusters in Bangladesh

The increasing occurrence of fire hazards in residential buildings in Bangladesh has led to severe property damage, physical injuries, and fatalities, underscoring a critical research gap in effective fire risk management. This study aims to address this gap by identifying key fire risks and proposing mitigation strategies using BIM technology. The research employs a comprehensive survey and scenario clustering methodology to identify these risks and assess the efficacy of various fire-resistant materials. Data collection involved questionnaires distributed to 200 respondents, including building owners, occupants, engineers, and firefighters. These clusters are based on the metrics of fatality numbers and property damage to quantify fire risk. Findings highlight the significant role of wall and ceiling lining materials (RII = 0.919) in contributing to fire risk. Further analysis was conducted using a residential building model developed in Autodesk Revit and simulated in Pyrosim software, assessing various lining materials. Quantitative results demonstrated that fire brick is the most effective material, exhibiting the lowest heat release rate (968 kW/m2), minimum adiabatic surface temperature (28°C), minimum heat transfer coefficient (1.75 W/m2/K), and lowest surface burning rate (7.5e−08 Kg/m2/s). These results suggest that fire brick is the optimal choice for enhancing fire resistance in residential buildings.

Optimizing Energy Efficiency with a Cloud-Based Model Predictive Control: A Case Study of a Multi-Family Building

The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation remains relatively unexplored, especially in the residential sector. The purpose of this research is to demonstrate the reliability, robustness, and computational efficiency of a cloud-based application of an MPC called Smart Energy Management (SEM) on a multi-family residential building. The SEM was tested on a virtual building model in TRNSYS using an open-source distributed event streaming platform for data exchange and synchronization. Simplified models for thermal behavior prediction, including an R3C3 model of the building, were developed in C++. The SEM was evaluated in eight scenarios with varying weather conditions, optimization criteria, and runtime periods. The results demonstrate that the SEM maintains stability and robustness over a 2-week period with a 15-minute planning resolution while ensuring thermal comfort. The C++ implementation of the optimization algorithm enables SEM deployment on low-spec servers, supporting cost-effective applications in real buildings with minimal intervention.

Analysis of the Efficiency of Using Direct Current in a Microgrid of a Residential Building

Analysis of the Efficiency of Using Direct Current in a Microgrid of a Residential Building

Analysis of Influential Factors of House Prices in Shenzhen

The change of house prices in Shenzhen can reflect the trend of house price changes in China, to a certain extent. This text chose the Factor Analysis Method to Specific analyze influential factors. This text put F1 as a variable, which included Total Floor Space of Residential Buildings Sold, Permanent Population, Total Floor Space under Construction of Commodity Housing, Investment in Real Estate Development, Gross Domestic Product, Local Budgetary Revenue and Expenditure. Through analyzing the data, this text deduced that Longgang, Baoan and Nanshan districts had above average values on F1. This text put F2 as a variable, which included Per Capital Disposable Income of Permanent Households and Growth Rate of Real Estate Investment, etc. Through analyzing the data, this text deduced that Nanshan, Luohu, Futian districts had above average values on F2. This text can conclude that Nanshan, Baoan and Futian Districts had a great influence on house prices in Shenzhen.

Behavior of Slab-on-Grade Resting on Expansive Soil

Abstract This paper discusses a case study of a residential building in Benghazi that features slab-on-grade foundations supported by expansive soils. Although such structures are uncommon in the area, they can suffer serious damage due to volumetric changes in the soil caused by wetting and drying. The building has a total plan area of 200 m², with tie beams located 1.2 m above the formation level, and expansive soil used as backfill. After being moistened and lightly compacted, 100 mm thick concrete slabs were poured between the tie beams, resulting in varying slab sizes. Following a month of heavy rainfall, significant cracking—longitudinal, diagonal, and warping—occurred in the slabs due to uneven deformation from the expansive soil. The paper includes photographs of these deformations and cracks, along with an analysis of the geotechnical properties of the expansive soil, including its swelling and shrinking characteristics. It also employs an approach to predict the pressure exerted on the slabs due to differential heave.

Study on prediction model of nitrogen emission in the production stage of residential building materials—A case study of Guangdong province

This study aims to reduce nitrogen emissions from residential buildings and establishes a prediction model for nitrogen emissions during the production of building materials. The calculation boundary, content, and method of nitrogen emission in the production stage of residential building materials are accurately analyzed. Based on the nitrogen emission data of 20 residential buildings in Guangdong, the composition and distribution characteristics of nitrogen emission in the production stage of building materials are analyzed. The coupling relationship between building design parameters and building materials’ nitrogen emissions is established using linear regression and ridge regression. 10 kinds of nitrogen emission prediction models based on the design parameters of residential buildings in the production stage of building materials were established and verified. The results show that the linear model M3, based on the number of floors above and below ground, the area width and depth, and the ridge regression model M5, based on the number of floors above and below ground, the area width and depth, and the total number of main functional rooms, have good fitting and prediction performance, respectively. The linear regression model M6, based on the number of floors above and below ground, the area width and depth, and the total number of rooms, has the best fitting and prediction performance. M3, M5, and M6 can accurately predict the nitrogen emission composition and distribution characteristics of building materials in residential building design and lay a foundation for future research on nitrogen emission evaluation and calculation methods and nitrogen emission reduction technology strategies.

PROCESSO DE APLICAÇÃO NO ASSENTAMENTO DAS PEÇAS EM CERÂMICAS

This study aims to explore the process of applying ceramic tiles, with a special focus on ceramic coatings used in high humidity environments, such as bathrooms and kitchens. The main objective is to deepen knowledge in the area of ​​ceramic coatings, understanding their properties, performance and implications for the durability of residential buildings. To answer this question, a detailed bibliographic review will be carried out on the ceramic materials available on the market, their technical characteristics and behavior in the face of humidity. In addition, case studies and experiments will be analyzed to assess the efficiency of ceramic coatings in preventing problems such as infiltration, mold and premature wear. The research will also include an analysis of the thermal properties of ceramic materials to understand their contribution to the energy efficiency of buildings. The expected results of this study aim to provide practical guidelines for civil engineers in the correct selection and application of ceramic coatings in humid environments. The findings are expected to help increase the lifespan of residential buildings and promote more efficient use of energy resources.

The impact of living environmental factors on cognitive function and mild cognitive impairment: evidence from the Chinese elderly population

ObjectivesMild cognitive impairment represents a pivotal stage in the cognitive decline of older adults, with a considerable risk of advancing to dementia. Recognizing how living environmental factors affect cognition is crucial for crafting effective prevention and intervention strategies. This study seeks to elucidate the relationship between various living environmental factors and cognitive function, with a specific focus on mild cognitive impairment, within a Chinese elderly population.MethodsThis is a cross-section and longitudinal study. Utilizing data from CHARLS, our cross-sectional analysis included 4,401 participants, while the cohort study comprised 3,177 individuals. We assessed living environmental factors based on household fuel types, water sources, indoor temperatures, residential building types, and ambient PM2.5 levels. We employed multiple linear regression for cross-sectional analyses and Cox proportional hazards regression models for longitudinal assessments to determine the effects of living environments on cognitive function and MCI risk. Stratified analyses, interaction tests, and sensitivity analyses were conducted to further validate our findings.ResultsThe findings revealed that, compared to those in high-risk environments, participants in low-risk settings exhibited higher cognitive scores (β = 1.25, 95%CI: 0.85, 1.65), better mental status (β = 0.70, 95%CI: 0.48, 0.92), and improved episodic memory (β = 0.27, 95%CI: 0.13, 0.41). Over a 7-year follow-up, the use of low-risk living environments (HR = 0.67, 95%CI: 0.49, 0.91), including clean fuels (HR = 0.74, 95%CI: 0.57, 0.95) and tap water (HR = 0.84, 95%CI: 0.71, 1.00), demonstrated a protective effect against MCI development. This correlation remained significant regardless of age, gender, residence, education level, smoking, alcohol consumption, and depression.ConclusionThis research provides substantial evidence that living environmental factors significantly affect cognitive function and MCI risk in Chinese older adults. Enhancing living conditions may be a key strategy for promoting cognitive health and preventing MCI in this demographic. Further research is necessary to explore the long-term impacts and potential intervention strategies to optimize living environments for better cognitive outcomes in aging populations.

Simultaneous Energy Optimization of Heating Systems by Multi-Zone Predictive Control—Application to a Residential Building

Climate change has made energy management a global priority. In France, the Grenelle Environment has set very ambitious progress targets for positive-energy buildings, particularly in terms of reducing and managing energy consumption. However, effective energy management in multi-zone buildings presents significant challenges, particularly when considering the inter-zone dynamics and heat transfer. This study examines multi-zone heating control, using a data-driven model for predictive indoor temperature modeling in intelligent buildings taking into account the influence of interconnected adjacent zones. The research methodology uses dynamic thermal simulation, parallel predictive models based on multiple linear regressions, and a multi-objective non-dominated sorting genetic algorithm II (NSGA-II) for the optimization process, which evaluates various generated heating strategies. This research introduces an approach to improve building energy efficiency by considering inter-zone dynamics and reducing heating-related energy consumption compared to a conventional heating strategy. By applying this model predictive control on a simulated case, a reduction in energy consumption due to heating is observed while respecting thermal comfort. This work contributes by implementing a method that independently controls temperatures in different building zones simultaneously while applying distinct constraints to each zone. This approach empowers occupants to manage heating consumption based on their preferences, ensuring personalized comfort. In addition, a comparison was made using a model that did not account for inter-zone interactions. This comparison demonstrates that incorporating these interactions into the predictive model enhances the effectiveness of the model predictive control approach. The multi-zone approach was also validated experimentally by using real experimental data, demonstrating significant reductions in energy consumption.

A Data Mining-Based Method to Disclose Usage Behavior Patterns of Fresh Air Systems in Beijing Dwellings during the Heating Season

As the popularity of fresh air systems (FAS) in residential buildings increases, exploring the behavioral characteristics of their use can help to provide a comprehensive understanding of the potential for demand flexibility in residential buildings. However, few studies in the past have focused on the personalized usage behavior of FAS. To fill this gap, this study proposes a method based on data mining techniques to reveal the behavioral patterns of FAS usage and the motivations behind them, including motivational patterns, operation duration patterns, and human–machine interaction patterns, for 13 households in Beijing. The simultaneously obtained behavioral patterns, in turn, form the basis of association rules, which can classify FAS usage behavior into two typical residential user profiles containing user behavioral characteristics. This study can not only provide more accurate assumptions and inputs for behavioral stochastic models but also provide data support for the development and optimization of demand response strategies.