基于CNN-LSTM混合神经网络的中期电力负荷预测方法OA

Medium-term electric load forecasting is a critical task in power system planning and scheduling,enhancing the operational efficiency and economic performance of power systems.However,due to the time dependence of electric load and the influence of multiple factors,achieving accurate load forecasting is challenging.To address this issue,it proposes a medium-term electric load forecasting model based on the combination of convolutional neural networks(CNN)and long short-term memory networks(LSTM).The model leverages the feature extraction capabilities of CNN and the advantages of LSTM in handling time series data,capturing the spatiotemporal dependencies in load data.Additionally,it employs Bayesian hyperparameter tuning to further optimize the model's hy-perparameters,improving prediction accuracy.Experimental results show that the proposed model outperforms traditional methods on multiple real-world transformer datasets,offering higher accuracy and robustness.