大连工业大学学报2026，Vol.45Issue(4)：285-292,8.DOI:10.19670/j.cnki.dlgydxxb.2026.0409
基于双向长短期记忆神经网络的冷库冷负荷预测算法
Cooling load forecasting algorithm of cold storage based on bidirectional long short-term memory neural network
王澄溪 1姜珊 1孙道鹏 2周鹏3
作者信息
- 1. 大连工业大学信息科学与工程学院,辽宁大连 116034
- 2. 大连冰山集团工程有限公司,辽宁大连 116100
- 3. 大连亚尼自控技术有限公司,辽宁大连 116031
- 折叠
摘要
Abstract
To address the challenges of accurate cold load prediction and energy consumption waste in small-scale Freon-based cold storage systems,an improved cold load forecasting model integrating Transformerand bidirectional long short-term memory neural networks(BiLSTM)(Transformer-BiLSTM-PLO-TSA)is proposed.This model replaces the original Transformer decoding layer with BiLSTM and fully connected layers,incorporates the polar light optimization algorithm(PLO)for parameter optimization,and integrates a spatio-temporal attention mechanism(TSA)to extract temporal-spatial correlation features from time-series data,thereby enhancing model robustness.A validation study demonstrated that the Transformer-BiLSTM-PLO-TSA model achieved RMSE of 2.57,MAE of 1.54,and R2 of 0.94,outperforming competing models such as BiLSTM,Transformer,Transformer-LSTM,Transformer-BiLSTM,and Transformer-BiLSTM-TSA in both accuracy and stability.关键词
冷负荷预测/双向长短期记忆网络/时空注意力机制/极光优化算法Key words
cold load forecasting/BiLSTM/temporal-spatial attention/polar lights optimization分类
信息技术与安全科学引用本文复制引用
王澄溪,姜珊,孙道鹏,周鹏..基于双向长短期记忆神经网络的冷库冷负荷预测算法[J].大连工业大学学报,2026,45(4):285-292,8.
