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

具有更高准确性和可解释性的多尺度建筑能源系统的基于物理的机器学习建模

• 批准号: 2725680
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2725680
• 关键词: Physics; informed; Machine; Learning; Modelling

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.

建筑物极大地有助于全球能源消耗和碳排放，并在加速低碳能源系统的转化中发挥重要作用。准确和透明的建模对于开发能源充满且有弹性的建筑物，表征建筑需求灵活性以及在实施之前全面评估不同的控制策略至关重要。实际上，基于物理的建筑建模需要许多投入，有些不确定。这通常会导致过度简单或不准确的方法，尤其是在大规模的多构建环境中。纯数据驱动的建模方法具有吸引力，但缺乏解释性，模型预测无法完全解释或可信。