Power Load Prediction Based on Pelican Optimized CNN-BiLSTM
In order to improve the accuracy of power load prediction,this paper proposes a new combined power load prediction model(POA-CNN-BiLSTM)based on the spatial feature extraction ability of convolutional neural networks(CNN),the predictive performance of bidirectional long short term memory(BiLSTM)networks in time series,and the optimization ability of the Pelican Optimization Algorithm(POA).Firstly,the feature vectors of the power load time se-ries are extracted using CNN,and then it is input into the BiLSTM network for bidirectional cyclic training to construct a CNN-BiLSTM prediction model.The POA is used to optimize the parameters of the BiLSTM network,such as the unit number of hidden layer,learning rate,and regularization coefficient.Finally,the power load prediction results are out-put.The proposed model is applied to forecast the power load in a certain arera.The results show that the prediction ac-curacy of BiLSTM and LSTM networks is better than that of LSSVM;The BiLSTM has higher prediction accuracy than LSTM networks;The optimization accuracy of POA is superior to particle swarm optimization algorithms(PSO);The prediction accuracy of CNN-LSTM and CNN-BiLSTM models is better than that of a single LSTM or BiLSTM models;The POA-CNN-BiLSTM model has the best prediction accuracy compared to the POA-LSSVM,PSO-LSTM,POA-LSTM,POA-BiLSTM and POA-CNN-LSTM models,which can better track the change trend of power load.
prediction of power loadPelican Optimization AlgorithmCNNBiLSTM
NETL
NSTL
万方数据
