Optimal control of parallel combination of heat pump and chiller system based on an improved golden jackal optimization

The energy consumption of heating, ventilation, and air conditioning (HVAC) systems accounts for over 40 % of total building energy use, emphasizing the need for effective control strategies. Existing research mainly focuses on optimizing the control of individual components, such as chillers or heat pumps. Limited research comprehensively addresses their parallel operation. This study proposed an improved golden jackal optimization (IGJO) algorithm, which enhances the global search capability and convergence performance of the original algorithm. Based on IGJO, a model predictive control optimization framework is developed for systems combining chillers and heat pumps. In the case study, the framework is applied to a heat pump-assisted chiller system in a commercial complex. The equipment model and load prediction model are constructed for optimal control. The results show that, under the IGJO strategy, the power fluctuations and equipment on/off switching are significantly reduced compared to baseline and genetic algorithm (GA) strategies. Furthermore, the IGJO strategy achieves significant energy savings while meeting cooling load demands. Compared to the baseline strategy, the total energy consumption is reduced by 5.21 % with the GA strategy and 12.8 % with the IGJO strategy.