An adaptive ensemble framework using multi-source data for day-ahead photovoltaic power forecasting

Abstract

Day-ahead photovoltaic (PV) power forecasting plays a crucial role in power market trading and grid dispatching. It has been empirically demonstrated in various fields that combining forecasts yields better results than using individual models. In this work, a novel adaptive ensemble framework is proposed based on multi-source data. First, incorporating prior information from physical models, three types of high-performance component models are constructed based on different types of data. Second, a multi-label classification method is utilized to select better performing models, allowing for switching between different model combinations depending on the weather conditions. Finally, a dynamic ensemble method is used to update the weights of the component forecasts based on its cumulative errors observed in the recent past. The proposed method was evaluated on a four-year PV multi-source dataset from 2019 to 2022. The forecasting skill (FS) in the test year (2022) reaches 50.61%. The results show that FS is improved by 4.75% compared to the optimal component model. Compared with other state-of-the-art methods, our method has achieved the best performance by improving FS at least 3.94%. The proposed framework in this study can be widely applied to other energy forecasting fields, such as wind/load forecasting.