The forecast of household power load based on genetic algorithm optimizing BP neural network

In view of the fact that power load forecasting is increasingly becoming an important part of power system planning and power dispatching, this paper proposed a forecasting method based on genetic algorithm optimizing BP neural network and applied it to household power load forecasting. First of all, the original data were pre-processed by factor analysis to eliminate the correlation between variables, and then the household load from January 1st to March 22nd was predicted by using BP and GA-BP models respectively. The author obtained the predicted results in the next 5 days summing to 120 hours and utilized MAPE and RMSE to compare their prediction errors quantitatively. The predicted results of the calculation example are in good agreement with actual values. Therefore it is demonstrated that the BP neural network optimized by genetic algorithm has better forecasting effects, which can provide reference for residential and municipal short-term load forecasting.