Flexible Building Energy Management With Neural ODEs-Based Model Predictive Control

The increasing renewable penetration leads to a great need for flexible resources to balance supply and demand. Building energy systems can provide considerable flexibility by optimally coordinating various appliances. However, the complex thermodynamics and insufficient data associated challenge the modeling and operation of building energy systems. To address this, this paper proposes a neural ordinary differential equations (neural ODEs) based model predictive control (MPC) framework for building energy management. Flexibilities are explored from two aspects: building thermal capacity and energy equipment coordination. The former is modeled using neural ODEs due to its complexity, while the latter is modeled as an energy hub. Both models are integrated into MPC in a linear form. The neural ODEs model is designed to strike a balance between reliability and representational capability, as well as a balance between the accuracy of long- and short-term predictions. The proposed method is verified on a simulated multizone retail building. The results indicate that the proposed model has a higher accuracy than the traditional resistance—capacitance (RC) model and the neural network model in operation with efficient computational performance. The controlled building can respond to price signals while providing demand response resources, which may result in significant cost savings.