An incremental photovoltaic power prediction method considering concept drift and privacy protection

Abstract

Concept drift (CD) and regional data sharing were considered potential factors critical to affecting data-driven model accuracy. In this paper, an incremental photovoltaic (PV) power prediction method is proposed for addressing the CD of streaming data and the privacy protection of regionally shared data. In the proposed scheme, a novel CD detection method is developed using Energy distance as a virtual drift criterion to measure the data distribution change and prediction error change as a real drift criterion to determine the drift time. In order to incorporate richer data features, this paper jointly trains a PV prediction model based on a broad learning system (BLS) through regional data sharing. Further, an incremental BLS model under the distributed federated learning (FL) framework is designed to maintain the privacy of each PV station's data. Finally, an incremental online model update strategy is proposed to learn new features continuously and without forgetting under the FL framework after CD occurs. The test results on the public dataset from DKASC, Alice Springs PV system, present that the proposed method can effectively detect the CD of streaming data and realize the fast online model update. Compared with the state-of-the-art techniques, the proposed method can significantly improve prediction accuracy while preserving privacy.