综合智慧能源2025，Vol.47Issue(2)：79-87,9.DOI:10.3969/j.issn.2097-0706.2025.02.008
基于CNN-LSTM-Self attention的园区负荷多尺度预测研究
Research on multi-scale load prediction in parks based on CNN-LSTM-Self attention
摘要
Abstract
Accurate load prediction is critical for improving the energy efficiency and profitability of zero-carbon smart parks.However,the application of conventional load prediction techniques faces two main challenges which are the difficulty in obtaining hourly numerical weather forecast data and the need for predictions across different time scales.In the absence of weather forecast data,a method using Convolutional Neural Networks(CNN)was proposed to extract the coupled spatial features between multiple loads.The reconstructed features were input into a Long Short-Term Memory(LSTM)network to extract temporal features of the load,followed by the application of a self-attention mechanism to enhance the model's ability to extract feature information.A fully connected network was then employed for load prediction,resulting in a multi-variable-load,multi-time-scale prediction model based on CNN-LSTM-Self attention.A case study of a park was used to predict its cooling,heating,and electrical loads for the next 1 hour,1 day,and 1 week.Experimental results showed that the CNN-LSTM-Self attention model outperformed the CNN,LSTM,and CNN-LSTM models in terms of prediction accuracy across multiple time scales.Specifically,the CNN-LSTM-Self attention model showed a more significant advantage in predicting the 1-hour load,with the mean absolute percentage error(MAPE)of cooling,heating,and electrical load predictions improved by 16.25%,19.16%,and 10.24%,respectively,compared to the CNN-LSTM model.关键词
零碳智慧园区/负荷预测/多时间尺度/卷积神经网络/长短期记忆神经网络/自注意力机制Key words
zero-carbon smart park/load prediction/multi-time scale/convolutional neural network/long short-term memory neural network/self-attention mechanism分类
信息技术与安全科学引用本文复制引用
杨澜倩,郭锦敏,田慧丽,黄畅,刘敏,蔡阳..基于CNN-LSTM-Self attention的园区负荷多尺度预测研究[J].综合智慧能源,2025,47(2):79-87,9.基金项目
南方电网公司科技项目(030100KC23020019) （030100KC23020019）
国家自然科学基金项目(52306013) （52306013）
中央高校基本科研业务费专项资金资助项目(21624212) Science and Technology Program of Southern Power Grid Company Limited(030100KC23020019) （21624212）
National Natural Science Foundation of China(52306013) （52306013）
Fundamental Research Funds for the Central Universities(21624212) （21624212）
