电力建设2024，Vol.45Issue(2)：90-101,12.DOI:10.12204/j.issn.1000-7229.2024.02.008
考虑数据分布偏移的短期居民净负荷预测方法
Research on Short-term Residential Net Load Forecasting Method Considering Data Distribution Shift
摘要
Abstract
As new energy generation incurs more uncertainty,it leads to a more severe shift in net load data distribution.The data distribution shift means that the feature information learned by the model in historical data is no longer fully applicable to future data,thus posing a significant challenge to net load forecasting(NLF).Therefore,considering the data distribution shift problem in net load,this study proposes a short-term residential net load forecasting method based on IRM-UW-LSTM to improve net load forecasting accuracy.First,a dual-objective problem was established using invariant risk minimization(IRM),which includes accurate forecasting and learning of invariant features across different data distributions.Second,a long short-term memory neural network(LSTM)was used to deal with the nonlinear features of the time series data.Subsequently,an uncertainty weighting(UW)-based objective balancing mechanism was used to avoid overachieving either objective.In addition,a quantile regression method was introduced to extend this study to probabilistic forecasting.Finally,the effectiveness of the proposed method was verified using multiple dimensions of deterministic and probabilistic prediction results,different data distribution shift levels,and different PV penetration rates based on real residential meter data provided by Ausgrid,Australia.关键词
短期居民净负荷预测/数据分布偏移/不变风险最小化/长短期记忆神经网络/不确定性加权Key words
short-term residential net load forecasting/data distribution shift/invariant risk minimization/long short-term memory neural network/uncertainty weighting分类
信息技术与安全科学引用本文复制引用
王瑞临,赵健,孙智卿,宣羿..考虑数据分布偏移的短期居民净负荷预测方法[J].电力建设,2024,45(2):90-101,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.51907114).国家自然科学基金项目(51907114) （No.51907114）
