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This study examines the utilisation of sophisticated predictive methodologies to enhance the energy efficiency and comfort of residential structures. The ASHRAE Global Thermal Comfort Database II was employed to construct and evaluate machine learning models that were designed to predict thermal comfort levels while optimising energy consumption. Air temperature, garment insulation, metabolic rate, air velocity, and humidity were identified as critical comfort determinants. Numerous predictive models were assessed, and XGBoost demonstrated improved performance as a result of hyperparameter optimisation (R2 = 0.9394, MSE = 0.0224). The study underscores the ability of sophisticated algorithms to clarify the complex relationships between environmental factors and occupant comfort. This sophisticated modelling methodology provides a practical approach to enhancing the efficiency of residential energy consumption while simultaneously ensuring the comfort of the occupants, thereby promoting more sustainable and comfortable living environments.

Abstract In the realm of architectural design and real estate development, a significant tension exists between design and finance. Design, with its emphasis on functionality, aesthetics, and human experience, often struggles to assert its value in a financial landscape dominated by quantifiable metrics. Traditional real estate valuation models frequently overlook the nuanced contributions of architectural design due to the absence of systematic approaches for identifying, measuring, and standardizing architectural data collection. This oversight has profound implications for the built environment, where design decisions must be financially justified to gain acceptance. The widening gap between design and finance in real estate development has fueled a trend toward the increasing financialization of new projects, often at the expense of design innovations that promote health, resilience, and sustainability. As the industry continues to prioritize evidence-based decision-making, there is an urgent need to develop systematic methods for measuring and evaluating design features to better understand their significance and contribution to economic value. This paper identifies and catalogs 19 building design features, surveys existing measurement techniques, and proposes methods for their measurement using existing tools and technologies.

Purpose>This study aims to explore the concept of green building by determining a suitable system for categorising green building features (GBFs) that are considered significant in enhancing the value of a building in a developing economy with particular reference to South Africa. The motivation for categorising the features is based on the perception that the upsurge in adopting green building and sustainability has ushered in a new and formidable set of challenges to practising professionals in terms of recognising the most significant value-adding GBFs.Design/methodology/approach>A quantitative approach was adopted, involving randomly selected construction professionals within the Western Cape Province of South Africa. The data were analysed using descriptive and inferential statistical analysis tools.Findings>Based on the mean ranking analysis, the top three most important features, amongst others, were kitchen and water-closet (WC) water efficient fittings, megawatt photovoltaic solar plant and water metering for monitoring and leak detection. Additionally, an exploratory factor analysis revealed that the underlying grouped features were “recycled materials and high-performance building energy design”, “water-saving and solar technologies”, “biometric system and acoustical feature”, “sensor control and natural daylight design”, “daylight harnessing feature”, “high-performance hydrologic strategy and noise control feature” and “special utility feature and water efficiency technologies”.Research limitations/implications>This study was conducted and limited only to the Western Cape Province of South Africa. However, the findings have practical significance to the generality of green building projects and may serve as a useful guide for other developing countries.Originality/value>This study broadens the viewpoint of construction professionals to recognise and prioritise the most important GBFs in South Africa that increase the value of a building. To create a system for assessing the sustainability of a building, the seven components and the features associated with them may be useful.

Energy renovation in buildings is one of the major challenges for the decarbonisation of the building stock. To effectively prioritise decision making regarding the adoption of the most efficient solutions and strategies, it is imperative to develop agile methods to determine the energy performance of buildings on an urban scale, in order to evaluate the impact of these improvements. In this regard, the data collection for feeding building energy models plays a key role in the accuracy and reliability of this issue, and the significant increase in recent years of available data from open data sources offers great potential in this respect. Thus, this study focuses on proposing a systematised and automated method for obtaining information from open data sources so as to obtain the most relevant geometric and thermal characteristics of residential buildings on an urban scale. The criteria for selecting the parameters to be obtained are based on their potential use as input data in different energy demand models aimed at assessing the energy performance of the building stock in a given area and, eventually, to evaluate the potential for improvement and the mitigation of different strategies. Geometric characterisation relies on obtaining and processing open data from cadastres to extract envelope surfaces categorised by orientation through QGIS (Free and Open Source Geographic Information System). For thermal characterisation, an automated process assigns different parameter-based information obtained from cadastral data, such as the year of construction. Finally, the applicability of the method is demonstrated through its implementation in the case study of Bilbao (Spain). The obtained results show that, although additional data should be collected when a detailed analysis of a building or building cluster has to be carried out, the existing open data can provide a first approximation, providing a first global view of the building stock in a region. It demonstrates the usability of the proposed method as an effective way to obtain and process these relevant data.

The aim of this paper is to highlight how the employment of Light Detection and Ranging (LiDAR) technique can enhance greatly the performance and reliability of many monitoring systems applied to the Earth Observation (EO) and Environmental Monitoring. A short presentation of LiDAR systems, underlying their peculiarities, is first given. References to some review papers are highlighted, as they can be regarded as useful guidelines for researchers interested in using LiDARs. Two case studies are then presented and discussed, based on the use of 2D and 3D LiDAR data. Some considerations are done on the performance achieved through the use of LiDAR data combined with data from other sources. The case studies show how the LiDAR-based systems, combined with optical Very High Resolution (VHR) data, succeed in improving the analysis and monitoring of specific areas of interest, specifically how LiDAR data help in exploring external environment and extracting building features from urban areas. Moreover the discussed Case Studies demonstrate that the use of the LiDAR data, even with a low density of points, allows the development of an automatic procedure for accurate building features extraction, through object-oriented classification techniques, therefore by underlying the importance that even simple LiDAR-based systems play in EO and Environmental Monitoring.

Improving energy efficiency is a major concern in residential buildings for economic prosperity and environmental stability. Despite growing interest in this area, limited research has been conducted to systematically identify the primary factors that influence residential energy efficiency at scale, leaving a significant research gap. This paper addresses the gap by exploring the key determinant factors of energy efficiency in residential properties using a large-scale energy performance certificate dataset. Dimensionality reduction and feature selection techniques were used to pinpoint the key predictors of energy efficiency. The consistent results emphasise the importance of CO2 emissions per floor area, current energy consumption, heating cost current, and CO2 emissions current as primary determinants, alongside factors such as total floor area, lighting cost, and heated rooms. Further, machine learning models revealed that Random Forest, Gradient Boosting, XGBoost, and LightGBM deliver the lowest mean square error scores of 6.305, 6.023, 7.733, 5.477, and 5.575, respectively, and demonstrated the effectiveness of advanced algorithms in forecasting energy performance. These findings provide valuable data-driven insights for stakeholders seeking to enhance energy efficiency in residential buildings. Additionally, a customised machine learning interface was developed to visualise the multifaceted data analyses and model evaluations, promoting informed decision-making.

Characterization of an existing building’s energy-related features is critical to inform maintenance and retrofit decisions. However, existing field-scale characterization methods tend to be labour intensive, invasive, and require high fidelity longitudinal data gathered through tightly regulated experiments. This highlights the need for a low cost, scalable, and efficient screening method. This paper puts forward a surrogate model-based approach to rapidly estimate energy-related building features. To this end, EnergyPlus models for 12 midrise office archetypes, all with a rectangular footprint, are developed. Ten thousand variants of each archetype are generated by altering envelope, causal heat gain, and heating, ventilation, and air conditioning operation features. A unique load signature is derived for each variant’s heating and cooling energy use. The parameters of the load signatures are clustered, then each cluster is associated with a set of plausible energy-related features. The accuracy of the results was evaluated using five test buildings not seen by the algorithm. The method could effectively identify building features with reasonable accuracy and no significant degradation in performance across all 12 archetypes.

Extracting and recognizing buildings from high-resolution remote sensing images faces many problems due to the complexity of the buildings on the surface. The purpose is to improve the recognition and extraction capabilities of remote sensing satellite images. The Gao Fen-2 (GF-2) high-resolution remote sensing satellite is taken as the research object. The deep convolutional neural network (CNN) serves as the core of image feature extraction, and PCA (principal component analysis) is adopted to reduce the dimensionality of the data. A correction neural network model, that is, boundary regulated network (BR-Net) is proposed. The features of remote sensing images are extracted through convolution, pooling, and classification. Different data collection models are utilized for comparative analysis to verify the performance of the proposed model. Results demonstrate that when using CNN to recognize remote sensing images, the recognition accuracy is much higher than that of traditional image recognition models, which can reach 95.3%. Compared with the newly researched models, the performance is improved by 15%, and the recognition speed is increased by 20%. When extracting buildings with higher accuracy, the proposed model can also ensure clear boundaries, thereby obtaining a complete building image. Therefore, using deep learning technology to identify and extract buildings from high-resolution satellite remote sensing images is of great significance for advancing the deep learning applications in image recognition.

Energy efficiency is a critical concern in built environment. Identifying key features that drive energy consumption is essential for optimizing building performance. Traditionally, studies have focused on single‐domain datasets. These approaches overlook the potential insights gained from integrating data across different domains. This research addresses this gap using a cross‐domain dataset that includes building characteristics, energy usage, and environmental factors. Feature selection techniques, including filter methods (correlation, mutual information), wrapper methods (RFE), embedded methods (Lasso, Random Forest, and gradient boosting), and dimensionality reduction are used to identify the most significant features contributing to the energy efficiency of residential properties. These techniques identify the most significant features influencing energy consumption. The findings show that cross‐domain features like energy consumption, CO2 emissions, and heating cost play a key role in predicting energy performance. By integrating data from multiple domains, the feature selection process reveals areas for energy optimization that are previously overlooked in single‐domain studies. The results provide valuable insights for energy consultants, building managers, and policymakers aiming to enhance energy efficiency in residential buildings. This research highlights the importance of cross‐domain data integration and offers a robust framework for feature selection. Ultimately, it contributes to more effectiveenergy‐saving strategies and sustainable building practices. The framework uses advanced linear and tree‐based techniques to analyze cross‐domain housing features and improve energy efficiency. Principal component analysis, recursive feature elimination, and random forest identify energy, emissions, cost, and building archetypes as critical factors. This novel pipeline promises transformative insights that will interest readers in further research.

Conventional methods of studying houses’ Energy Performance Certificates (EPCs) typically fail to investigate the impact of interrelated contextual elements instead fixating exclusively on the specific attributes of individual houses. This study presents a new method that combines network graph analytics (NGA) with interactive visual analytics to investigate hidden linkages at the individual house level. Our proposed platform collects and analyses data related to housing attributes, creates a network based on the links between these attributes, and employs sophisticated graph algorithms to provide visual representations. Users have the ability to dynamically choose postcodes, metrics, and attributes, which, in turn, generate layouts of networks that provide valuable insights. The visualisation utilises colour gradients and node metrics to improve the comprehensibility of energy performance areas. The platform enables homeowners and stakeholders to comprehend the interrelationships between aspects such as neighbouring housing features, and house infrastructure. The results prove the efficacy of the strategy by giving a collection of case studies that encompass various Energy Performance Certificates (EPCs) ranging from A to G. Each case study demonstrates the evolution of network architectures and visual assessments, showcasing the energy performance linked to certain EPC ratings. The platform offers a user-friendly interface for stakeholders to investigate and understand attribute relationships.