Jiahao Wen, Zhijian Wang*
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 749-765, 2023, DOI:10.32604/cmes.2023.023865
Abstract Since the existing prediction methods have encountered difficulties in processing the multiple influencing factors in short-term power load forecasting, we propose a bidirectional long short-term memory (BiLSTM) neural network model based on the temporal pattern attention (TPA) mechanism. Firstly, based on the grey relational analysis, datasets similar to forecast day are obtained. Secondly, the bidirectional LSTM layer models the data of the historical load, temperature, humidity, and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network, so that the influencing factors (with different characteristics) can select relevant information from different time steps… More >
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