A New Hybrid Model using WTBPNN-EHO and LSTM with EMD-WOA Signal Decomposition for mid and Long-Term Electricity Load Forecasting

Mesbaholdin Salami, Masoumeh Rostam Niakan, Masoud Hasani Marzooni, Kioumars Heydari


The manager of the electricity supply chain needs to correctly forecast one of the most important variables affecting the management of the electricity chain. This article has proposed a hybrid model for electricity demand forecasting using deep learning. Firstly, the historical electricity demand data is decomposed using empirical mode decomposition (EMD) algorithm. Then, whale optimization algorithm (WOA) is used to determine signal decomposition levels rounds by EMD and the allocation of signals to neural networks. Parameters that promote accuracy of the forecast are selected using principal component analysis (PCA). A group of signals are fed into a back propagation neural network, whose components are decomposed by wavelet transform. The weights of this neural network are determined by using elephant herding optimization (EHO) algorithm (WTBPNN-EHO). The rest of the signals with higher levels of complexity are fed into the long short-term memory (LSTM) neural network. Finally, the load is calculated by aggregating the results of these two neural networks. Finally, the performance of this model has been compared with other existing models


Deep learning; Electricity demand forecasting; LSTM; Signal decomposition; WOA

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