基于多策略改进金豺算法优化LSTM的短期电力负荷预测OA北大核心CSTPCD

There are problems of low accuracy and poor stability in short-term load forecasting using long short-term memory(LSTM)neural networks.Thus this paper proposes an improved golden jackal optimization(IGJO)algorithm to optimize the LSTM model.First,it integrates a convex lens reverse learning strategy for better starting positions.It introduces the sigmoid function to change the escape energy and balance exploration and development stage.It fuses whale optimization algorithm's spiral enclosure to improve exploration capability and convergence accuracy.Then,it introduces the LSTM neural network,and uses the IGJO algorithm to optimize its hyperparameters and to establish the IGJO-LSTM short-term electricity load forecasting model.Finally,the IGJO-LSTM short-term load forecasting model is validated using actual power load data from a region in Henan province.The experimental results show that the short-term load prediction results of the IGJO-LSTM model at different times on weekdays and weekends are closer to the actual load.Compared to traditional methods,it demonstrates higher accuracy and stability,indicating practical application potential.