提出一种基于多参数特征关联(multi parameter feature association
MPFA)内嵌物理神经网络(physics-informed neural network
PINN)的园区空调负荷短期预测模型。通过MPFA方法对多维数据进行聚类标记,提取特征向量作为模型输入。基于空调热力学物理方程和深度神经网络构建PINN模型,在模型中嵌入物理约束,实现对园区空调负荷的预测。以商业园区空调负荷数据为例,验证了所提方法的有效性。
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