Renewable energy system based on IFOA-BP neural network load forecast

Abstract

In order to improve wind energy utilization and the accuracy of load forecasting, a wind turbine model was established and parameters were optimized. The back propagation neural network based on improved fruit fly optimization algorithm (IFOA-BP) is applied to load forecasting. Aiming at the optimization problem that the fruit fly optimization algorithm is easy to fall into the local or global optimum during the optimization process, use the improved fruit fly optimization algorithm to increase the search distance first to improve the diversity of the fruit fly optimization algorithm population, and then reduces the search distance, to enhance its search ability. Taking the load data of a power plant as an example, the algorithm was simulated and analyzed in matlab/simulink. The simulation results show that the algorithm can improve the prediction accuracy of wind energy and load.