南方电网技术2023，Vol.17Issue(12)：71-79,9.DOI:10.13648/j.cnki.issn1674-0629.2023.12.009
基于偏差补偿TCN-LSTM和梯级迁移策略的短期风电功率预测
Short-Term Wind Power Prediction Based on Deviation Compensation TCN-LSTM and Step Transfer Strategy
摘要
Abstract
As the proportion of new energy in power systems gradually increases,new energy power prediction becomes a research focus.However,the power prediction of the new-built wind farm is faced with the problem of historical data insufficiency,and dif-ficulty in feature transfer.A short-term wind power prediction approach based on the deviation compensation TCN-LSTM and step transfer strategies are proposed.First of all,the small amount of data of the target wind farm are divided into two groups according to the correlation with the source wind farm.Then the historical data of the source wind farm is used to train the hybrid model contain-ing the error compensation module.Finally,the model is constructed with the step transfer strategy.The relevant case analysis of this paper exhibits that the prediction accuracy of the compensation step transfer learning model based on TCN-LSTM is increased by 1.23%compared to similar direct prediction models.The effectiveness of the proposed approach is proved by related cases.关键词
时间卷积网络-长短时记忆网络/短期风电功率预测/偏差补偿/梯级迁移策略Key words
TCN-LSTM/short-term wind power prediction/deviation compensation/step transfer strategy分类
信息技术与安全科学引用本文复制引用
宋技峰,彭小圣,杨子民,段睿钦,周彬彬,陈凯,王有香..基于偏差补偿TCN-LSTM和梯级迁移策略的短期风电功率预测[J].南方电网技术,2023,17(12):71-79,9.基金项目
中国南方电网有限责任公司科技项目(YNKJXM20210100) （YNKJXM20210100）
国家重点研发计划资助项目(2022YFB2403000). Supported by the Science and Technology Project of China Southern Power Grid Co.,Ltd.(YNKJXM20210100) （2022YFB2403000）
the National Key Research and Development Program of China(2022YFB2403000). （2022YFB2403000）
