Two-layer Optimal Strategy for Signal Decomposition and Prediction Model Combination in Load Forecasting of Distribution Network
The increasing volatility and nonlinear characteristics of load time series put forward higher requirements for load forecasting methods.The conventional combined forecasting method has application limitations for massive load data.Therefore,the two-layer optimization strategy is proposed for time series decomposition method and prediction model combination in load forecasting of distribution network.Firstly,for a certain load forecasting data,the weights are configured in the time series signal decomposition layer,optimized by minimizing the root mean square error of the load,to obtain the optimal combination scheme of time series signal decomposition method.On this basis,the proposed strategy optimizes the combination scheme at the prediction model layer,and obtains the optimal combination of each prediction model by configuring the weight coefficient to further improve the accuracy of load forecasting.The simulation results show that the proposed strategy can optimize the combination of signal decomposition methods and prediction models according to the load characteristics,and reduce the influence of non-stationarity load sequence on prediction accuracy in distribution network.
distribution networkprediction modeltime series signal decompositiontwo-layer optimizationcombined forecasting
万方数据
维普
