An energy efficiency optimization control method of warehouse environmental equipment based on an improved differential evolution algorithm

Abstract

To address the challenges of coordinated control between air conditioners and fans in warehouse environments, as well as the energy waste caused by lagging manual operations, an energy efficiency optimization control method for warehouse environmental equipment based on an improved differential evolution(DE) algorithm is proposed in this paper. First, using multi-point ambient temperature sensor data, the equipment start/stop decision problem is transformed into a constrained multivariable optimization problem, and an objective function for warehouse energy consumption optimization is constructed. Second, an improved DE algorithm is proposed based on a self-adaptive mechanism for scaling factor F and crossover probability CR parameters and a diversity threshold triggering mechanism (DTSaDE), via considering the spatial diversity and fitness diversity of individuals comprehensively. Then, DTSaDE is employed to search for the optimal configuration of control parameters. Finally, simulation experiments are conducted in a typical warehouse environment scenario. Compared with the traditional manual experience-based control strategy, the proposed method saves approximately 23.07% of the total system energy consumption, achieving refined and intelligent management of warehouse environmental equipment operation and providing an effective technical pathway for data-driven warehouse energy-saving optimization.