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Rapid Machine Learning-Based Energy Prediction for BIPV-Integrated Modular Buildings

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The accelerating global transition toward carbon neutrality demands rapid and reliable energy prediction tools for innovative building systems such as Building-Integrated Photovoltaic (BIPV) modular buildings. Conventional physics-based simulation methods, while accurate, are computationally intensive and unsuitable for real-time design optimization. This study proposes a novel machine learning-based rapid energy prediction methodology tailored specifically to the thermal and geometric characteristics of modular BIPV-integrated buildings. Feature Engineering for Modular Building Representation A comprehensive feature engineering framework was developed to capture the distinctive attributes of modular construction. The approach incorporates six-surface thermal property encoding, geometric parameters, and detailed solar irradiance calculations to represent envelope exposure and inter-module interactions. This structured encoding ensures that key thermal behaviors and photovoltaic infl...
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Climate-Specific Optimization of Phase Change Material Glazing for Energy-Efficient Office Buildings

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Buildings remain major contributors to global carbon dioxide emissions, intensifying the urgency of envelope innovations that enhance energy efficiency and climate resilience. As extreme weather conditions increasingly challenge conventional passive design strategies, adaptive façade technologies have gained prominence. Phase Change Material (PCM) glazing offers a promising approach by increasing thermal inertia, reducing cooling demand, and improving indoor comfort. However, its performance under diverse climatic and operational contexts remains insufficiently quantified. Multivariate Design Framework and Simulation Scope This study conducts a comprehensive multivariate analysis of PCM glazing across ten representative climates in Europe and North America. Key design variables include window-to-wall ratio (WWR), façade orientation, and PCM type, evaluated under two internal heat gain scenarios. A validated heat transfer model integrated into EnergyPlus™ was used to perform 4,320 si...
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Particle-Packing Optimized Aerogel–Perlite–Cement Composite Mortars for High-Performance Building Envelopes

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Enhancing the thermal efficiency of building envelopes while preserving mechanical integrity remains a central challenge in sustainable construction. Conventional insulation mortars often struggle to simultaneously optimize low thermal conductivity, moisture resistance, and adequate structural strength. This study develops aerogel–perlite–cement (ACM) composite insulation mortars through particle packing optimization to achieve balanced thermal, mechanical, and hygric performance, while also evaluating their building-scale energy implications. Material Design Based on Particle Packing Optimization ACM mortars were formulated using the modified Andreasen & Andersen particle packing model with distribution moduli of q = 0.2 and 0.3 to optimize aggregate gradation. A silica aerogel slurry was incorporated into expanded perlite carriers to enhance insulation performance while maintaining structural cohesion. This design strategy aims to minimize pore connectivity for improved therma...
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Time-Dependent Performance Trajectories of Residential Buildings under Climate Change

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  Residential buildings designed to comply with winter-oriented efficiency standards increasingly struggle to maintain safe indoor conditions during summer heat stress and under long-term climate change. Conventional regulatory frameworks treat building performance as a static compliance outcome rather than a dynamic trajectory shaped by irreversible structural design decisions. This study reconceptualizes residential performance as a time-dependent process influenced by envelope mass, ground coupling, and evolving climatic conditions. Experimental Design and Monitoring Framework The research is based on full-scale experimental monitoring of two identical residential test buildings differentiated by envelope thermal mass and floor-ground interaction. Continuous performance measurements were conducted to capture seasonal energy behavior and indoor thermal responses, particularly under extreme summer conditions. This empirical foundation enables robust comparison of structural des...
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Improving Simulation Accuracy of VRF Heat Pump Systems through Manufacturer-Informed Performance Modeling

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Accurate modeling of cooling and heating systems is fundamental to reliable building energy simulation, particularly for high-performance buildings targeting low-carbon objectives. Variable Refrigerant Flow (VRF) heat pump systems are widely adopted due to their flexibility and efficiency; however, simulation accuracy strongly depends on the quality of performance input parameters. This study investigates how different performance curve inputs influence the predictive reliability of VRF system simulations in EnergyPlus. Testbed Validation and Model Calibration A validated experimental testbed model, calibrated using field measurements from a controlled test facility in South Korea, was employed to ensure realistic baseline performance. The calibrated model provided a reliable reference for comparing simulation outputs under varying input curve assumptions, enabling quantitative assessment of modeling accuracy. Comparison of Performance Input Configurations Three simulation configu...
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Coordinated EV Integration for Enhancing Energy Flexibility in Rural Net-Zero Energy Buildings

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Net-zero energy buildings (NZEBs) are designed to achieve annual balance between renewable energy generation and consumption. However, real-time mismatches persist due to photovoltaic (PV) intermittency and inflexible building loads. Electric vehicles (EVs), equipped with mobile storage capacity and bidirectional charging capabilities, offer significant flexibility potential to mitigate these temporal imbalances. This study investigates coordinated EV integration strategies within a rural solar-powered NZEB context. Integrated Energy System Configuration and Operational Scenarios An integrated energy system combining grid interaction, PV generation, stationary battery storage, EV charging infrastructure, and building loads was developed. Three operational strategies were designed: Case 1 (rule-based control), Case 2 (building-to-vehicle, B2V), and Case 3 (vehicle-to-building combined with building-to-vehicle, V2B + B2V). These scenarios enable comparative evaluation of unidirectiona...
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Data-Driven Diagnosis of Building Performance Aging and Its Impact on HVAC Energy Consumption

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Accurate forecasting of building energy consumption is essential for achieving carbon neutrality, enhancing operational efficiency, and maintaining long-term building performance. While significant attention has been given to short-term prediction models, the progressive effects of building performance aging particularly on HVAC energy consumption—remain insufficiently quantified. This study addresses this gap by developing a robust data-driven methodology to diagnose aging-related performance degradation using long-term operational datasets. Long-Term Dataset and Integrated Analytical Framework The research is based on ten years (2015–2024) of continuous operational data from a university educational building in Chongqing, China. The dataset integrates sub-metered HVAC energy records, detailed meteorological observations, and occupancy-related proxy variables. This comprehensive data fusion enables the isolation of performance aging effects from climatic and behavioral variability,...
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