Short-Term Load Combination Forecasting Based On Load Decomposition And Multivariable Neural Network

Short-term load forecasting(STLF)is an important means of power market economy,safe operation and dispatching of power system.Accurate STLF can not only help relevant departments and enterprises to reasonably arrange the start and stop of units and formulate power purchase plans,but also provide valuable suggestions for users.The change of shortterm load has the dual characteristics of global cycle and local random,and is affected by many variables such as meteorology.It is difficult to capture the change rule by a single prediction method.Therefore,this paper proposes a combined optimization method combined with load decomposition prediction and multivariable neural network prediction,which integrates load forecasting information obtained from different models from different angles to improve the accuracy and stability of STLF.From the perspective of load variation,STLF is carried out by load sequence decomposition.Firstly,the improved singular spectrum analysis decomposition method(ISSA)is used to decompose the power load into trend load and random load.Then,the seasonal difference autoregressive moving average model(SARIMA)is selected to predict the stability and periodicity of the trend load,and the long short-term memory neural network model(LSTM)is used to predict the nonlinear characteristics of the random load.Finally,the predicted values of the two models are superimposed to form the final load forecasting value,and the STLF method based on ISSA-SARIMA-LSTM(ISSL)is constructed.This method can accurately characterize the short-term load changes and improve the stability of STLF.Starting from a variety of external factors affecting load change,STLF is carried out by combining multiple environmental variables closely related to load change.Firstly,the influence of various environmental variables on the load is analyzed,and then the correlation between variables and load changes is analyzed by the grey correlation method with variable resolution coefficients.The multi-variable input LSTM neural network prediction model(M_LSTM)is constructed.The hidden and nonlinear relationship between various environmental variables and load changes is mapped through model training to improve the prediction accuracy of STLF.In order to reduce the prediction risk of STLF and improve the prediction accuracy of STLF,a variable weight combination optimization prediction model based on induced ordered weighted averaging operator(IOWA)is established.According to the difference of prediction information obtained by ISSL and M_LSTM models from different angles,the variable weight combination weights model orderly according to the prediction accuracy of the two methods at different points,so as to realize the complementary advantages and disadvantages of different methods.The experimental data show that compared with the constant weight combination model such as the reciprocal variance method,the variable weight combination optimization can more comprehensively reflect the variation law of short-term load,and effectively improve the stability and practical application ability of the prediction model.