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Reducing energy use within the building sector is vital to create sustainable cities and mitigate global warming. Urban building energy modelling (UBEM) is useful to evaluate energy demand and renovation potential in districts. In this paper, an archetypes-based calibration approach for UBEM is introduced to evaluate the effect of various timescales for energy data and detail of building-related data on the energy performance of multi-residential buildings. A case district in northern Sweden was used to identify the impact of various refurbishment strategies at district-scale.

The applicability of the archetypes-based calibration approach was validated by comparing the district model performance with high-resolution energy data. Calibrating the archetypes-based model with monthly resolution data showed a similar outcome as using higher-resolution data. Further, a district model with less archetypes can reduce modelling time and complexity, while performing similarly as a model consisting of more archetypes with higher detail. The results suggest that simplifications to UBEM can be used without compromising the performance accuracy and might facilitate district modelling in situations with data limitations.

The findings in this study contribute to the knowledge on UBEM of existing districts and energy efficiency measures’ impact on district-level energy performance.

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.

Characterizing the energy performance of building components and locating anomalies is necessary for effectively refurbishing existing buildings. It is often challenging because defects in building envelopes deteriorate without being visible. Passive infrared thermography (PIRT) is a powerful tool used in building inspection. However, thermal image interpretation requires significant domain knowledge and is prone to artifacts arising from a complex interplay of factors. As a result, PIRT-based inspections require skilled professionals, and are labor-intensive and time-consuming. Artificial intelligence (AI) holds great promise to automate building inspection, but its application remains challenging because common approaches rely on extensive labeling and supervised modeling. It is recognized that there is a need for a more applicable and flexible approach to leverage AI to assist PIRT in realistic building inspections. In this study, we present a label-free method for detecting anomalies during thermographic inspection of building envelopes. It is based on the AI-driven prediction of thermal distributions from color images. Effectively the method performs as a one-class classifier of the thermal image regions with a high mismatch between the predicted and actual thermal distributions. The algorithm can learn to identify certain features as normal or anomalous by selecting the target sample used for training. The proposed method has unsupervised modeling capabilities, greater applicability and flexibility, and can be widely implemented to assist human professionals in routine building inspections or combined with mobile platforms to automate the inspection of large areas.

We've devised an interpretable stochastic integrated machine learning method for predicting thermal conductivity in Polymeric Graphene-Enhanced Composites (PGECs) across different scales. Our approach, integrating techniques like Regression Trees methods (Gradient Boosting machine), handles uncertain parameters via a bottom-up multi-scale framework using Representative Volume Elements in Finite Element Modeling (RVE-FEM). To understand factors affecting predictions, we use the SHapley Additive exPlanations (SHAP) algorithm. This method aligns well with training data, offering promise for efficiently designing new composite materials for thermal management.

Modelling the performance of building stocks is crucial in facilitating the renovation at the building stock level. Bottom-up building stock modelling begins by detailing individual buildings and then aggregates them into stock level. Its primary advantage lies in capturing the inherent heterogeneity among distinct buildings, which enables tailored retrofitting. Naturally, this approach requires a comprehensive dataset with detailed building information such as geometry and envelope thermal properties. However, a common challenge is the incompleteness of available data in individual datasets. To address this, previous bottom-up studies have filled the missing data with representative or statistical data. Such practice could lead to homogeneous modelling of distinct buildings within the same statistical group. This limits the utilization of key ability of bottom-up building stock modelling in capturing heterogeneity, such as tailored retrofitting to explore potential retrofitting areas and strategies. To address this challenge of homogeneous modelling, we utilize data fusion framework for bottom-up building stock modelling, employing probabilistic record linkage and inverse modelling techniques to integrate multiple incomplete building performance datasets. This framework fills the missing data in one dataset with information from another, thus capturing inherent heterogeneity in the building stock. An empirical study was conducted in Umeå, Sweden, to investigate the framework's effectiveness by modelling building stock with various retrofitting strategies. This study contribution lies in enhancing bottom-up building stock modelling by capturing inherent heterogeneity, to provide tailored retrofitting solutions.

Polyurethane (PU) possesses excellent thermal properties, making it an ideal material for thermal insulation. Incorporating Phase Change Materials (PCMs) capsules into Polyurethane has proven to be an effective strategy for enhancing building envelopes. This innovative design substantially enhances indoor thermal stability and minimizes fluctuations in indoor air temperature. To investigate the thermal conductivity of the Polyurethane-Phase Change Materials foam composite, we propose a hierarchical multi-scale model utilizing Physics-Informed Neural Networks (PINNs). This model allows accurate prediction and analysis of the material’s thermal conductivity at both the meso-scale and macro-scale. By leveraging the integration of physics-based knowledge and data-driven learning offered by Physics-Informed Neural Networks, we effectively tackle inverse problems and address complex multi-scale phenomena. Furthermore, the obtained thermal conductivity data facilitates the optimization of material design. To fully consider the occupants’ thermal comfort within a building envelope, we conduct a case study evaluating the performance of this optimized material in a detached house. Simultaneously, we predict the energy consumption associated with this scenario. All outcomes demonstrate the promising nature of this design, enabling passive building energy design and significantly improving occupants’ comfort. The successful development of this Physics-Informed Neural Networks-based multi-scale model holds immense potential for advancing our understanding of Polyurethane-Phase Change Material’s thermal properties. It can contribute to the design and optimization of materials for various practical applications, including thermal energy storage systems and insulation design in advanced building envelopes.

Most people who have moved to high-altitude areas temporarily suffer from sleep disorders. Sleep deprivation negatively affects not only people's daytime activities but also their health. However, most of the existing nonpharmaceutical intervention methods have the problems of discomfort, restricted movement, or high cost. This study involved the use of an oxygen-rich flow of air in the breathing area during sleep to fight hypoxia and aid with altitude acclimatization when people first traveled to a highland plateau. The associated nasal breathing targeted oxygen supply system (NBTOSS) was designed and optimized by numerical simulation and full-scale experiments. Blood oxygen saturation (SaO2) and pulse rate (PR) monitoring experiments were conducted on subjects exposed to hypoxia at a high altitude (Lhasa, 3646.31 m) with or without assistance from the novel oxygen system and on a lowland plain (Xi'an, 397.5 m) as a comparison. The size of the affected area, concentration target value, and oxygen consumption were used as evaluation indices. Experiments have demonstrated the feasibility of creating an oxygen-enriched microenvironment in breathing area during sleep. The results of the testing showed that the oxygen supply area was uniformly covered and that the degree of hypoxia in subjects was effectively alleviated, with average SaO2 increasing to 95% ± 1%. Maintaining oxygen levels during sleep for temporary residents of high altitudes with less oxygen consumption and minimal oxygen supply costs is discussed to provide a healthy and comfortable oxygen-enriched environment.

We introduce an interpretable stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene-enhanced composites (PGECs). This method encompasses the propagation of uncertain input parameters from the meso to macro scale, implemented through a foundational bottom-up multi-scale framework. In this context, Representative Volume Elements in Finite Element Modeling (RVE-FEM) are employed to derive the homogenized thermal conductivity. Besides, we employ two sets of techniques: Regression-tree-based methods (Random Forest and Gradient Boosting Machine) and Neural networks-based approaches (Artificial Neural Networks and Deep Neural Networks). To ascertain the relative influence of factors on output estimations, the SHapley Additive exPlanations (SHAP) algorithm is integrated. This interpretable machine learning methodology demonstrates strong alignment with published experimental data. It holds promise as an efficient and versatile tool for designing new composite materials tailored to applications involving thermal management.

This study investigates the influence of a microencapsulated Phase Change Material (mPCM) on building systems in a subarctic climate which is not commonly studied for PCM applications. The mPCM is incorporated into gypsum to make a composite board with a volume fraction of 30 vt%. The fabricated composite board is then used to make a box model. This model along with a reference model built only with gypsum boards are placed inside a climate chamber where temperature is regulated to a summer day of a subarctic country, where large temperature variation exists between day and night. In addition, a Finite Element Method (FEM), is also used for the validation of the experimental data. The thermal-physical properties of the mPCM gypsum board including the specific heat capacity and thermal conductivity are measured. The microscopic features of the composite board are also studied. In addition, the temperature variation and the thermal energy storage of the boards of the two models have been studied. Results indicate that incorporation of mPCM into gypsum will change the thermal properties of the material. PCM can work as an additional insulation layer due to its low thermal conductivity. Further, the temperature fluctuation inside of the model with mPCM is reduced. In addition, the energy stored in the mPCM composite is around 3 times higher than that of gypsum board, making it promising for building energy improvement and load shifting.

Evaluating and comparing the performances of different strategies is critical for energy-efficient building retrofitting. Data-driven modelling based on large building performance datasets is an effective method for such evaluations. However, it could be challenging to apply this approach to buildings from data-scarce areas where local building performance datasets have not been well-established, which means the data falls short of the high demand for building retrofitting on a global level. To address this, a transfer learning approach is proposed in this study that can evaluate the performance of buildings without local well-established building performance datasets. The proposed approach is applied in the Swedish-Chinese empirical study that relies on the Swedish dataset to transfer and predict the building performance in China without well-established datasets. It was achieved by applying fuzzy C-means clustering and a neural network (FCM-BRBNN) to pre-train the evaluation model based on the Swedish dataset. Then, the proposed approach collects a small sample of Chinese buildings in the data-scarce area and transfers the model to local building performance prediction. The results show that the transfer learning approach can reliably predict the performance of building retrofitting in data-scarce areas with only hundreds of local building samples. As such, this study provides a novel methodology that can support the evaluation and comparison of retrofitting strategies in data-scarce regions and countries with only limited local data. It could efficiently assist designers in optimizing energy-efficient designs in the pre-retrofit stage. Crucially, the methodology enables the transfer of knowledge regarding building performance across different countries and regions, being pivotal for the international collaboration required to stimulate the global energy-efficiency transformation.