Multi-chiller scheduling using reinforcement learning with transfer learning for power consumption prediction

Abstract

Conventionally, chiller power consumption has been optimized by using a Cooling Load based Control (CLC) approach which does not consider the impact of one control strategy on the other. Embodiments of the present disclosure provide reinforcement learning (RL) based control strategy to perform both chiller ON/OFF sequencing as well as setpoint leaving chilled water temperature (LCWT) scheduling. A RL agent is trained using a re-trained transfer learning (TL) model and LCWT, return chilled water temperature of target chillers and ambient temperature of building are read for determining required cooling load to be provided by target chiller(s) based on which the target chillers are scheduled for turning ON/OFF. Transfer learning-based approach is implemented by present disclosure to predict power consumed by a chiller at some setpoint by using a model trained on similar chillers which were operated at that setpoint since the chillers are usually run at a single setpoint.