Data-driven application on the optimization of a heat pump system for district heating load supply: A validation based on onsite test

Abstract

Heat pump system is widely used in district heating because of its high energy efficiency and economic benefits. Because of the complexity of large heat pump systems, achieving optimal operation in practical project remains challenging. Model predictive control is a promising method for operation optimization. This paper represents a data-driven optimization framework based on model predictive control for optimal district energy supply. Four sub-models of the framework including heating load prediction model, heat pump performance model, main pump performance model and indoor state prediction model were built via machine learning and fitting methods. The particle swarm optimization algorithm was adopted to determine the optimal operation strategy. The framework’ effectiveness was verified by the operation data of a district energy system in Shanghai by means of both offline and onsite validation. The optimization results under low load conditions show that the energy-saving ratio can reach 9.43%, correspondingly, 12.37% for medium load conditions and 4.50% for high load conditions. In onsite validation, the Mean Absolute Percentage Error and Root Mean Square Error of heating load prediction are 6.72% and 104.21 kW, respectively. Compared with the day under similar weather conditions, the framework helps operators to obtain 12.9% energy-saving ratio in practice.