考虑数据分布偏移的短期居民净负荷预测方法OACSTPCD

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.