东北电力技术2026，Vol.47Issue(6)：1-6,27,7.
基于CNN-LSTM混合神经网络的中期电力负荷预测方法
CNN-LSTM-Based Hybrid Neural Network for Medium-Term Electric Load Forecasting
摘要
Abstract
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.关键词
电力负荷预测/深度学习/CNN/LSTMKey words
electric load forecasting/deep learning/CNN/LSTM分类
信息技术与安全科学引用本文复制引用
刘硕,丁宇昂,赵梓焱..基于CNN-LSTM混合神经网络的中期电力负荷预测方法[J].东北电力技术,2026,47(6):1-6,27,7.基金项目
国家自然科学基金项目(62203093) （62203093）
辽宁省科技计划联合计划(基金)项目(2023-MSBA-074) （基金）
