综合智慧能源2026，Vol.48Issue(4)：35-46,12.DOI:10.3969/j.issn.2097-0706.2026.04.005
基于改进CNN-LSTM和多目标霜冰算法的综合能源系统优化调度
Optimal scheduling of integrated energy systems based on improved CNN-LSTM and multi-objective RIME algorithm
摘要
Abstract
To improve the forecast accuracy and operation economic efficiency of integrated energy systems under multi-energy coupling conditions,an electric-thermal load forecasting and optimal scheduling method was proposed based on an improved convolutional neural network and long short-term memory neural network(CNN-LSTM).The method integrated CNN and LSTM,introduced an attention mechanism to enhance the capabilities of feature extraction and temporal response,and achieved the coordinated optimization of the forecasting model and operation strategy in combination with the multi-objective rime optimization algorithm(MORIME).Simulation results based on typical summer and winter days of a park in Jinan showed that the improved CNN-LSTM model achieved a 32.6％reduction in the mean absolute percentage error of load forecasting compared to that of the traditional model.With MORIME,the total operation costs of the system in summer and winter reduced by 12.7％and 10.3％respectively,and the energy utilization efficiency of the system increased by 3.8％to 4.1％.The efficient coordination of the electric-thermal energy storage system and the time-shift utilization of energy significantly improve the operation economic efficiency and energy utilization efficiency of the system,thereby providing a new technical approach for the optimal scheduling of complex multi-energy systems.关键词
综合能源系统/负荷预测/卷积神经网络/长短期记忆神经网络/注意力机制/多目标霜冰算法Key words
integrated energy system/load forecasting/convolutional neural network/long short-term memory neural network/attention mechanism/multi-objective rime optimization algorithm分类
能源科技引用本文复制引用
朱丽娟,刘吉营,于明志,杨开敏,毛煜东..基于改进CNN-LSTM和多目标霜冰算法的综合能源系统优化调度[J].综合智慧能源,2026,48(4):35-46,12.基金项目
国家重点研发计划项目(2024YFE0106800)National Key R&D Program Project(2024YFE0106800) （2024YFE0106800）
