Physics-informed Machine Learning Modelling for Multi-scale Building Energy Systems with Enhanced Accuracy and Interpretability

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Buildings significantly contribute to global energy consumption and carbon emissions and play an important role in accelerating the transformation of a low-carbon energy system. Accurate and transparent modelling is essential for developing energy-flexible and resilient buildings, characterising building demand flexibility, and comprehensively assessing different control strategies before implementation. In practice, physics-based building modelling requires many inputs, some uncertain; this often leads to overly simplistic or inaccurate approaches, especially in large-scale multi-building contexts. Pure data-driven modelling approaches are attractive but lack interpretability, and model predictions are not fully explainable or trustable.