Short-term power load forecasting model based on multi-strategy improved WOA optimized LSTM
Qian Liang,
Wencheng Wang,
Yinchao Wang
Abstract:Accurate short-term power load forecasting is essential to balance energy supply and demand, thus minimizing operating costs. However, power load data possesses temporal and nonlinear characteristics, and to mitigate the effects of these factors on the prediction results, we introduce the Long Short-Term Memory neural network (LSTM, Long Short-Term Memory). However, the performance of the LSTM algorithm is highly dependent on the pre-set parameters, and relying on empirically set parameters will make the model… Show more
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