Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression

Demand response (DR) of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids. In this special fast DR event, effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment. This study, therefore, developed a data-driven model predictive control (MPC) using support vector regression (SVR) for fast DR events. According to the characteristics of fast DR events, the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance. Meanwhile, a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls. Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously. Compared with RC-based MPC, the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.

Automated summary

This study developed a data-driven model predictive control using support vector regression for fast demand response events in commercial buildings. By optimizing SVR hyperparameters and shortening the genetic algorithm search range, the proposed SVR-based MPC successfully achieved simultaneous control of power demand and indoor temperature. Compared with RC-based MPC, the SVR-based MPC reduced time/labor costs without sacrificing control performance in fast DR events.