Optimization Of Load Forecasting Based On RNA-CS Algorithm

Load forecasting has always been a key topic in power system research and an important basis for guiding macro-control.At present,the empirical value method or statistical method is mainly used for load forecasting,and the forecast is based on historical data,so there is a large forecast error.The main purpose of load forecasting is to reduce load peaks and increase load valleys,thereby reducing the difference between peaks and valleys and reducing the impact effect of the power system.Therefore,the study of the accuracy of load forecasting plays a vital role.Based on the characteristics of each distributed power supply,the paper builds a distributed power supply model for microgrids such as wind power generation and photovoltaic power generation.At the same time,a two-level scheduling model is designed for the power system including microgrids.Simulation analysis provides mathematical calculation models.In order to improve the accuracy of power load forecasting and reduce its peak-to-valley difference,this paper combines the characteristics of excellent global search ability of the RNA global search capability and the horizontal group algorithm CS,and uses the RNA high and low mutation operators to optimize CS Levi’s flight.A hybrid RNA-CS algorithm;using test functions for simulation.The results show that the hybrid RNA-CS algorithm exhibits better optimal values than other algorithms and has a faster convergence rate.The thesis uses RNA-CS hybrid algorithm to predict the power of wind power and photovoltaic power generation.The simulation results show that RNA-CS is more accurate in predicting the power of wind power and photovoltaic power generation than the basic genetic algorithm and cuckoo algorithm.Based on the participation of microgrid distributed power supply in the main grid load forecasting,the paper uses RNA-CS hybrid algorithm to optimize the parameters of the two-level dispatch model of the power system including the microgrid.The valley difference effectively improves the accuracy and stability of the forecast,reduces the accident rate of the power grid,and thus reduces economic losses.