Demand response control of heating system in office building based on adapted neutral temperature in hot summer and cold winter climate zone of China

The thermal history of the human body influences indoor neutral temperature requirements, especially during the winter. However, existing demand response (DR) controlled heating systems do not fully account for this. To quantify the impact of thermal history, 36 participants with different thermal histories were exposed to 20 °C, 16 °C, and 12 °C. A predictive model for adaptive neutral temperatures, based on thermal history as input, was then developed and validated. A multi-objective model predictive control (MPC) framework, incorporating the Non-dominated Sorting Genetic Algorithm II (NSGA-II), was implemented to optimize energy flexibility, energy cost, and thermal comfort by considering the hourly energy pricing market in China. The optimized results were further evaluated in an office building using IDA Indoor Climate and Energy (IDA ICE) simulation software. The results showed that the MPC significantly increased the flexibility factor (FF) without sacrificing thermal comfort, as thermal history and adaptation effects were found to shift the neutral temperature by approximately 0.4 °C for every 1 °C change in thermal history. Additionally, the MPC reduced energy costs by approximately 32 %, 26 %, and 41 % for normal buildings, extra-insulated buildings, and old buildings, respectively. Therefore, it is recommended to implement an MPC strategy based on DR control and adaptive neutral temperature for building heating in areas where occupants have diverse thermal histories.