Electricity Consumption Prediction with Variance-based Optimum Searching Weighting
In recent years,with the impact of climate change and the irregularity of electricity consumption changes,high-er requirements have been put forward for monthly electricity prediction.Individual prediction methods cannot achieve ideal prediction results,so it is necessary to consider the optimization weighting through hybrid prediction methods to im-prove accuracy.By analyzing and exploring the regularity of historical data,this work sought various models suitable for predicting development patterns,conducted methodological study on variance-based optimum searching and weighting,and obtained comprehensive prediction results.First the historical electricity consumption of agricultural indicators in a certain province was extracted and multiple algorithms were selected for prediction.Second all the independent predicted values were integrated into historical data for year-on-year and month-on-month variance calculation.The average variance of both of the sequence variance values were automatically weighted according to the degree of fluctuation,and the final weighted prediction result was obtained.The proposed method was applied for practical prediction,which verified its ef-fectiveness and high prediction accuracy.
NETL
NSTL
万方数据
