A data-driven method for the optimal control of centralized cooling station in an office park

Abstract

An effective way to reduce the energy consumption of a building is to optimize the control strategy for the HVAC system. Load prediction is suggested and used to match the supply and demand for air conditioning and achieve energy savings. However, the gap between load prediction models and real-time optimal control of HVAC systems still exists. Hence, this paper proposed an optimization method for dynamically determining the best setpoints of chillers and chilled water pumps under a specific load. The energy consumption model of each equipment in the centralized cooling station is established and validated using the operational data. Then an optimization problem is defined to find the optimal setpoints for each equipment under certain load, to realize the lowest energy consumption. To verify the validity of the proposed method, a period of real operational data in an office park is used. The proposed method is applied on one centralized cooling station in the office park and results in an 4% lower overall energy consumption than the existing intelligent control strategies in the park. This method provides feasible directions and reference for realizing overall optimal control of the whole HVAC system in the future.