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

随着新能源在电力系统中的占比逐渐提高,新能源功率预测成为一个研究热点。但对于新建的风电场的功率预测则面临历史数据不足和特征迁移困难的问题。因此提出了一种基于偏差补偿TCN-LSTM和梯级迁移策略的短期风电功率预测方法。首先,将目标风电场的少量数据根据与源风电场的相关性大小分为两组,然后利用源风电场历史数据训练含有误差补偿模块的复合模型,最后以梯级迁移学习策略进行建模。相关算例分析表明基于TCN-LSTM的补偿梯级迁移模型预测精度相比同类直接预测模型提升1.23%。相关算例证明了所提出的方法的有效性。

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.