排灌机械工程学报2026，Vol.44Issue(5)：479-487,9.DOI:10.3969/j.issn.1674-8530.24.0025
基于大型水电机组实测数据的LSTM-SVM抬机预测
LSTM-SVM-based lifting prediction for large hydropower units using measured data
摘要
Abstract
For large mature hydropower plants,predicting the lift amount based on massive historical data of unit lifting during transient processes can effectively circumvent mathematical modeling difficul-ties and provide a feasible approach for lift amount prediction.Extensive on-site measurement data from unit transient processes in a large hydropower plant on the Jinsha River Basin was selected,and an LSTM-SVM hybrid algorithm model(combining long short-term memory network and support vector machine)was constructed.Through data classification,training,validation,and testing,an optimized lift amount prediction model was obtained,and targeted model evaluation indicators were designed.The prediction results of the model were compared with on-site measured data,while comparative expe-riments using the BiLSTMalgorithm and the single LSTMalgorithm were conducted to analyze the pre-dictive advantages of the proposed algorithm.Experimental results show that the LSTM-SVMhybrid al-gorithm achieves a prediction accuracy of over 98%,demonstrating excellent lift amount prediction per-formance.关键词
大型水电机组/抬机/LSTM-SVM/预测/现场数据/人工智能Key words
large-scale hydropower unit/lifting/LSTM-SVM/prediction/on-site measured data/artificial intelligence分类
农业科技引用本文复制引用
蒋树,钱晶,李佳,曾云,鲍友洪..基于大型水电机组实测数据的LSTM-SVM抬机预测[J].排灌机械工程学报,2026,44(5):479-487,9.基金项目
国家自然科学基金资助项目(52269020,52079059) （52269020,52079059）
