The Method Research And Application Of Improve Accuracy Of Maoming Region Of Short-term Load Forecasting

The dispatching of power system for a certain area in the short term power load forecasting is a basic and very important work,and the precision of forecasting model to maintain regional power supply power balance,reduce frequent key,improve the power supply network security and so on,all have important influence.Due to the influence factors of power load is more,using a single forecasting model can not achieve good prediction effect.Short-term power load historical data based on maoming region,with the improved particle swarm optimization algorithm to multiple single forecasting model together,thus improve the short-term load forecasting accuracy in maoming region.This paper first introduces the basic theory knowledge of power load,including the concept of power load and power load forecasting principle and the characteristics of power load forecasting analysis and the factor affected the accuracy of power load forecasting method and error analysis,etc.And then elaborates the model and method for the forecast of the current mainstream and analysis,including the forecast method based on grey theory,based on the BP neural network prediction method,the prediction method based on regression analysis and prediction method of using the least square method.Single forecast model is applied to power load forecasting when the effect is not ideal,in the full analysis of maoming region short-term power load,on the basis of statistical data,with the improved particle swarm optimization algorithm to multiple prediction model together,solve the single forecast model in maoming area to the problem of shortage of short-term load forecasting accuracy.Through the analysis of single forecasting Model in power load forecasting effect,with the improved particle swarm optimization algorithm of the BP neural network forecasting Model,grey theory prediction Model and Autoregressive Moving Average Model(Autoregressive Integrated Moving Average Model,bsde ARIMA)three single forecasting Model combined together,analysis of the combination forecast Model prediction effect.In maoming region is analyzed in the combination forecast model to 24 h and 2 weeks of electronic load case,the results show that the improved particle swarm optimization algorithm of the prediction accuracy of combination forecasting model is superior to the single forecasting model,and the combination of good adaptability of the model.