Research On Power Load Forecasting Based On Multiple Time Scale Analysis

The accurate short term load forecasting is significant for power grid planning and economic development.Based on the popular machine learning algorithms,this paper studies the short term load forecasting.Firstly,analyses the characteristics and fluctuations of the historical electric load data with the multi time scale analysis technique.Secondly,proposes a combined model based on genetic algorithm(GA),WNN,RBFNN and ENN for short-term power load forecasting.Thirdly,builds one NAR neural network for short-term power load forecasting.Finally,two hourly electricity load datasets(Dec.01,2016 to Nov.30,2017,Pennsylvania—New Jersey—Maryland,American;Dec.01,2016 to Nov.30,2017,the State of New South Wales,Australia)are selected to test the effectiveness and generalization ability of the combined and NAR models.The main contents and results of this paper are as follows: First,according to the characteristics of PJM load data in American,analyses its periods by complex wavelet analysis theory.The results show that there are 4 kinds of period variations,which are 52 days,33 days,17 days and 7 days,respectively.Second,according to the result of multiple time scale analysis,the first period is used to build the combined forecasting mode,which is NAR neural network optimized by GA.Third,the combined model is used to analyze and forecast the Pennsylvania—New Jersey—Maryland,American load data from Dec.01,2016 to Nov.30,2017.And the forecasting result is tested by actual load data.The results show that the R values of combined forecasting model and NAR neural network model are both 0.976,SMAPE are 1.4% and 1.7%,respectively.For the State of New South Wales,R values of combined model and NAR neural network model are 0.973 and 0.979 respectively,and SMAPE are 1.4% and 1.8% respectively.The effective short term load forecasting provides theoretical support for formulating reasonable grid planning and ensuring stable operation of the grid system.In terms of time series analysis,the forecasting results show that the proposed combined forecasting model based on GA and NAR neural network model are effective for short term load forecasting.