基于实例迁移学习的小样本光伏功率短期预测

FEW-SHOT PHOTOVOLTAIC POWER SHORT-TERM FORECASTING BASED ON INSTANCE TRANSFER LEARNING

针对新建光伏电站历史数据匮乏导致功率预测精度不足的问题,提出一种基于实例迁移学习的小样本光伏发电功率短期预测方法。首先,以一组丰富的长期运行光伏数据为源域,利用多核最大均值差异估计源域与目标域光伏数据的匹配相似性,筛选出高相似的迁移源域;然后,建立加权对抗双向长短期记忆网络,通过对抗学习赋予源域光伏样本权重以调整其数据分布,将调整后的源域数据充实目标域数据集,采用双向长短期记忆网络挖掘公共知识域中光伏发电功率序列与气象数据的双向时序关联特性,实现小样本条件下光伏功率的精准预测。结果表明:相较于传统深度学习和模型迁移方法,所提方法能有效提高历史数据有限条件下光伏功率的预测精度。

To address the problem that the prediction accuracy of photovoltaic power is insufficient due to the lack of historical data of newly-built photovoltaic power stations,an instance transfer learning-based short-term prediction method is proposed for few-shot photovoltaic power generation.Firstly,a set of rich long-term operation photovoltaic data is used as the source domain,and then the multi-kernel maximum mean discrepancy was employed to estimate the matching similarity of photovoltaic data between source domain and target domain,and the migration source domain with high similarity was screened out.Then,a weighted adversarial bi-directional long-short time memory network was established.The photovoltaic samples in the source domain were weighted to adjust their data distribution by adversarial learning,and the adjusted source domain data was enriched to the target domain dataset.The bi-directional long-short time memory network was used to mine the bi-directional time sequence correlation of photovoltaic power sequence and meteorological data in the public knowledge domain,so as to achieve accurate prediction of few-shot photovoltaic power.The results show that the proposed method can effectively improve the prediction accuracy of photovoltaic power under the limited historical data compared with the traditional deep learning and model transfer methods.