基于CEEMD-ITSA-BiLSTM组合模型的短期负荷预测OA

Accurate short-term load forecasting of power system is helpful to flexible planning of system re-sources,reasonable scheduling of units,and improvement of system operation efficiency.In view of the accuracy of load forecasting,this study proposes a short-term load forecasting model based on CEEMD-ITSA-BiLSTM(Com-plete Ensemble Empirical Mode Decomposition-Improved Tunicate Swarm Algorithm-Bidirectional Long Short-Term Memory).CEEMD decomposition is carried out on the time series load data to obtain several stable IMF(In-trinsic Mode Function),and BiLSTM modeling and prediction are carried out for each IMF.To improve the accuracy of BiLSTM,ITSA algorithm is used to optimize the parameters of the super parameters such as the number of hidden layer nodes,learning rate and training times of BiLSTM,and CEEMD-ITSA-BiLSTM load forecasting model is es-tablished.The simulation experiment is conducted with the actual load data,and the single BiLSTM model and the BiLSTM model optimized by different algorithms are compared.The results show that the RMSE(Root Mean Square Error),MAE(Mean Absolute Error)and MAPE(Mean Absolute Percentage Error)error indexes of CEEMD-ITSA-BiLSTM model are increased by 48.54%,51.32%and 44.78%,respectively when compared with the BiL-STM model,and are significantly lower than other comparison models.