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Building-specific excitation optimization and simulation-driven machine learning for rapid in situ identification of the heat loss coefficient

https://doi.org/10.2139/ssrn.6928008
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MGMI: A novel deep learning model based on short-term thermal load prediction
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Review of methods for assessing the measured heat transfer coefficient (HTC)
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Identifying suitable models for the heat dynamics of buildings
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Co-heating test: A state-of-the-art
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QUB: A fast dynamic method for in-situ measurement of the whole building heat loss
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An artificial intelligence framework for predicting operational energy consumption in office buildings
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ISSA-LSTM: A new data-driven method of heat load forecasting for building air conditioning
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Mapping indoor overheating exposure rate at city scale: A data-driven method based on building typologies and surrogate-modelling
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An Optimal Surrogate-Model-Based Approach to Support Comfortable and Nearly Zero Energy Buildings Design
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Heat loss identification method for heating building exterior walls based on BO-TCN-LSTM-SA
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A novel deep-learning framework for short-term prediction of cooling load in public buildings
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Metaheuristic optimization of roof designs to enhance energy performance and thermal comfort using parametrization and machine learning
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Enhancing Short-Term Power Load Forecasting for Industrial and Commercial Buildings: A Hybrid Approach Using TimeGAN, CNN, and LSTM
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