A decoupling estimation method for baseline load of photovoltaic user clusters based on multidimensional abnormal data restoration

Abstract

Abstract With the widespread installation of distributed photovoltaics, their fluctuation characteristics with weather changes have posed significant challenges to baseline load estimation. In addition, due to measurement and collection anomalies, it is easy to cause continuous missing and abnormal data collection. To address the issue of low baseline estimation accuracy caused by distributed photovoltaic fluctuation characteristics and abnormal data collection, this paper proposes a photovoltaic user cluster baseline load decoupling estimation method based on multi-dimensional abnormal data repair. Firstly, a multidimensional anomaly data repair system is established based on extreme learning machines and multi-step prediction. Secondly, using the repaired data for baseline estimation, photovoltaic users and non-photovoltaic users are decoupled and separated. Long short-term memory neural network estimation models are used for non-photovoltaic users, and baseline load estimation methods based on weather type analysis are used for photovoltaic users. Finally, the baseline results are accumulated. The simulation results show that compared with the direct estimation method of baseline load, the accuracy of decoupling estimation has improved by 33.7%.