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Demand response (DR) is a key strategy for enhancing energy flexibility, allowing buildings to dynamically adjust electricity demand and mitigate supply–demand mismatches—particularly in the context of rising renewable energy integration. Single-family houses (SFHs) are increasingly recognized as decentralized energy actors in advancing DR, owing to their suitability for integrating on-site generation systems such as photovoltaic (PV) panels. In such houses, an energy management system (EMS) coordinates local generation and consumption through DR optimization methods. Due to the high autonomy of single-family houses, effective DR optimization is critical for facilitating occupant participation, especially as thermal comfort significantly affects engagement. Although research in this domain is expanding, a systematic review focusing on DR optimization for SFHs with on-site generation and thermal comfort integration has yet to be conducted. To fill this gap, this review systematically synthesizes existing DR optimization methods in accordance with the PRISMA guidelines. DR optimization approaches are categorized into five groups: rule-based control, mathematical programming, metaheuristic optimization, model predictive control, and artificial intelligence-based methods. It also classifies thermal comfort integration approaches into four types: comfortable temperature zone (CTZ), comfortable temperature deadband (CTD), PMV–PPD, and adaptive thermal comfort models. A mechanistic framework integrating thermal comfort into DR optimization is developed, and a six-dimensional analysis reveals key methodological trade-offs and emerging trends. Finally, the review highlights key research gaps and outlines future directions, including refined thermal comfort metrics, occupant-centric and behavior-aware optimization frameworks, and uncertainty-aware strategies to ensure robust and scalable DR deployment in single-family houses.

Polymer nanocomposites (PNCs), particularly those enhanced with graphene fillers, show significant promise for improving thermal conductivity in applications such as electronics, energy storage, and aerospace. However, accurately predicting their thermal behavior remains challenging due to the complex multiscale interactions between fillers, polymer matrices, and interfacial regions. This study proposes a novel data-driven framework that integrates Gaussian Process Regression (GPR), sensitivity analysis, correlation analysis, and quantitative causal inference to model and interpret the thermal conductivity of polymer graphene-enhanced composites (PGECs). The GPR model demonstrates strong predictive performance (R2 = 0.8931) while also providing calibrated uncertainty estimates. Sensitivity and correlation analyses identify matrix conductivity and filler volume fraction as dominant factors influencing thermal transport. To move beyond associative insights, causal inference is applied, revealing that thermal matrix, volume fraction, and aspect ratio have direct causal impacts on conductivity, whereas other variables such as Kapitza resistance and graphene conductivity do not. By combining stochastic multiscale modeling with causal reasoning, this approach enhances both predictive accuracy and interpretability, offering a robust framework for material design and performance optimization.

Enhancing energy efficiency in the building sector is vital for climate change mitigation. Phase change materials (PCM), known for their high latent heat storage capacity during phase transitions, offer promising potential in regulating indoor temperatures and reducing dependence on HVAC systems. This study presents a preliminary experimental investigation into the thermal performance of wallboards enhanced with PCM under subarctic conditions in Umeå, Sweden. Four types of wallboards were tested: a conventional gypsum board, a commercial PCM board (cPCM), and two lab-fabricated boards with 60% and 82% microencapsulated PCM by mass (mPCM60 and mPCM82). Thermal properties of the PCM were characterized using Differential Scanning Calorimetry (DSC), confirming high latent heat capacity and thermal stability. In a field setup, surface temperatures were monitored to assess thermal buffering capacity. Results show lower temperature fluctuations in the mPCM82 and mPCM60 wallboards, indicating enhanced thermal performance of the envelope compared to the gypsum board. However, the thermal performance of cPCM was even worse than gypsum, which could be attributed to the observed material settlement. These findings suggest the potential of PCM-enhanced wallboards to improve indoor thermal comfort and energy performance, while also emphasizing the importance of material selection and design in practical applications.

Polyurethane (PU) as a popular polymer is widely recognized for its exceptional thermal insulation properties, making it a critical material for applications requiring effective heat transfer management. Integrating Phase Change Materials (PCMs) into PU (PU-PCMs) has emerged as a highly effective strategy for enhancing building envelope performance, ensuring greater indoor thermal stability, and mitigating temperature fluctuations. This study introduces an Enhanced Multi-Scale Modeling approach to investigate the thermal conductivity and energy efficiency of PU-PCMs, with a particular focus on their comparative performance in subarctic and tropical climates. By combining Molecular Dynamics (MD) simulations with Finite Element Methods (FEM), the model seamlessly bridges molecular-scale interactions with macroscopic thermal behavior. Using a Representative Volume Element (RVE)-FEM framework, microscopic properties are translated into engineering-scale parameters, enabling accurate and efficient predictions of material performance across diverse climatic conditions. The findings demonstrate the capability of PU-PCMs to significantly enhance energy efficiency and indoor thermal comfort. In a case study of a single-family house, PU-PCMs achieved a 7.376% reduction in energy consumption and increased comfort hours under Stockholm's subarctic climate. Moreover, the adaptability of PU-PCMs was validated in both tropical and subarctic environments, consistently stabilizing indoor temperatures, reducing HVAC energy demands, and improving occupant comfort. These results underscore the potential of PU-PCMs as a passive thermal management solution, advancing sustainable building practices. The proposed multi-scale model offers a computationally efficient and precise tool for optimizing material design in energy-sensitive applications, reinforcing the versatility and significance of PU-PCMs across a range of climatic conditions.

Existing operation methods of air conditioning and mechanical ventilation system ignore actual occupant distribution, resulting in energy waste due to overcooling and overventilation. To create an acceptable indoor environment while reducing energy consumption of operation, occupant-centric control (OCC) strategy has been proposed and developed. In this study, the proposed OCC strategy adjusts on/off of air supply vents and sub-zone air supply parameters according to sub-zone occupancy, involving two sub-zone air supply volume allocation methods, so as to prioritize thermal comfort and air quality in local occupied zone. Computational fluid dynamics was employed to evaluate the OCC strategy's performance in the case of applying stratum ventilation. The results show that the predicted mean vote at 0.6 m height is kept at 0.29–0.53, and the CO₂ concentration at 1.1 m is controlled below 1100 ppm, which can achieve energy savings of 18–51 % (compared to Baseline 1) and 4–16 % (compared to Baseline 2). Moreover, the larger the difference in the number of occupants between sub-zones, the more energy savings the OCC strategy can achieve. It is suggested that sub-zone air supply volume should be allocated according to sub-zone cooling load and the outdoor air ratio in critical sub-zone should be employed to compensate for total outdoor air volume. This study provides an approach to combine the OCC strategy with non-uniform air distribution, offering insights into the balance of energy efficiency and occupied environmental comfort in system operation.

The advancement of artificial intelligence (AI) in material design and engineering has led to significant improvements in predictive modeling of material properties. However, the lack of interpretability in machine learning (ML)-based material informatics presents a major barrier to its practical adoption. This study proposes a novel quantitative computational framework that integrates ML models with explainable artificial intelligence (XAI) techniques to enhance both predictive accuracy and interpretability in material property prediction. The framework systematically incorporates a structured pipeline, including data processing, feature selection, model training, performance evaluation, explainability analysis, and real-world deployment. It is validated through a representative case study on the prediction of high-performance concrete (HPC) compressive strength, utilizing a comparative analysis of ML models such as Random Forest, XGBoost, Support Vector Regression (SVR), and Deep Neural Networks (DNNs). The results demonstrate that XGBoost achieves the highest predictive performance ((Formula presented.)), while SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) provide detailed insights into feature importance and material interactions. Additionally, the deployment of the trained model as a cloud-based Flask-Gunicorn API enables real-time inference, ensuring its scalability and accessibility for industrial and research applications. The proposed framework addresses key limitations of existing ML approaches by integrating advanced explainability techniques, systematically handling nonlinear feature interactions, and providing a scalable deployment strategy. This study contributes to the development of interpretable and deployable AI-driven material informatics, bridging the gap between data-driven predictions and fundamental material science principles.

Graphene-based polymer nanocomposites show great potential for thermal management, but accurately predicting their thermal conductivity remains challenging due to multiscale structural complexity and parameter uncertainty. We propose an innovative approach integrating interpretable stochastic machine learning with multiscale analysis to predict the macroscopic thermal conductivity of graphene-based polymer nanocomposites. Our bottom-up framework addresses uncertainties in meso- and macro-scale input parameters. Using Representative Volume Elements (RVEs) and Finite Element Modeling (FEM), we compute effective thermal conductivity through homogenization. Predictive modeling is powered by the XGBoost regression tree-based algorithm. To elucidate the influence of input parameters on predictions, we employ SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), providing insights into feature interactions and interpretability. Sensitivity analyses further quantify the impact of design parameters on material properties. This integrated method enhances prediction accuracy, reduces computational costs, and bridges data-driven and physical modeling, offering a scalable solution for designing advanced composite materials for thermal management applications.

The utilization of immersive virtual environments (IVEs) has emerged as a pivotal tool in enhancing observation of occupant-building interaction (OBI) in non-existing and pre-operational buildings (e.g., buildings under-designed, renovated, and retrofitted). The data derived from IVEs are critical in developing Building Predictive Models (BPMs) that prioritize occupant comfort and optimize building performance. Nevertheless, a persistent challenge is the collection of sufficiently large sample sizes from IVEs, often resulting in data sets inadequate for creating accurate and dependable BPMs. To address the gap, the generation of synthetic data is one promising solution. Mega-trend diffusion (MTD) is particularly adept at managing the nuances of small, mixed-type, and imbalanced data sets aligning with the natures of the IVE data sets. This study explores MTD-based algorithms such as baseline MTD, baseline MTD with class probability function, and k-Nearest Neighbors MTD (kNNMTD), all of which are adept at addressing the inherent data challenges. Various small data sets associated with OBI in IVEs were used to test these algorithms. The fidelity of the synthetic data sets is assessed using the Pairwise Correlation Difference (PCD) and accuracy of Artificial Neural Networks (ANNs) trained on the synthetic data sets with several modeling structures. A variety of findings indicated strength and limitations of the algorithms, where some areas need further investigation. At this stage, the evaluation based on this study found that the kNNMTD produced synthetic data sets that were closest to the experimental data set (i.e., the smallest PCD), contributing to the most accurate ANN models.

Occupant energy-related behaviors such as activities, interactions, and occupancy are widely acknowledged as key drivers of building performance. These behaviors are highly individualized and influenced by factors such as building characteristics, lifestyle preferences, technological adoption, and demographic differences. The associated profiles, consisting of multiple behaviors performed continuously over time, are commonly used as inputs for building performance simulations (BPS) to model occupancy dynamics, predict energy demand, and optimize performance. Due to limited profiles’ acquisition and synthetization, traditional BPSs often rely on static or average assumptions that fail to account for individual contexts, causing bias and unreliability. To address the limitation, this study demonstrates a framework that incorporates time-use surveys to observe occupant energy-related behavior profiles and employs a Graph Attention and Long Short-Term Memory (GAT-LSTM) networks for profile prediction. It was validated through a case study using Swedish time-use survey data from six occupants. The GAT-LSTM model achieved high validation accuracy (96–98%) and demonstrated strong agreement between predicted and next-day behavior profiles, with Cohen’s Kappa values ranging from 0.479 to 0.638.

This study offers a comprehensive review of the state-of-the-art developments regarding the application of phase change materials (PCMs) into building envelopes and HVAC systems. Incorporating PCMs into building envelopes can significantly enhance thermal storage capacity and reduce the influence of outdoor temperature fluctuations on interior spaces' thermal conditions. Furthermore, combining PCM-enhanced building envelopes with night ventilation can effectively utilize natural cooling resources, thereby improving both the adaptability and efficiency of PCMs in regulating indoor thermal environments and reducing building energy consumption. An alternative approach for energy conservation involves integrating PCMs directly within ventilation and air conditioning systems. This review offers insights and recommendations for future research, highlighting the necessity of further developments in materials science, system optimization, and real-world application studies to maximize the potential of PCMs in the built environment.