Model‐agnostic online forecasting for PV power output

Abstract

A reliable forecasting model is required for photovoltaic (PV) power output because solar energy is highly volatile. Another driver for the need of a reliable forecasting model is concept drift, which means that the statistical properties of the data change over time. In this paper, an online forecasting method to handle concept drift is proposed. First, the problem of forecasting in batch learning is transformed into a forecasting in online learning setting. Then, an online learning algorithm is applied, which is good for handling concept drift. Through experiments using the real-world data, it is shown that the method noticeably improves performance compared to the case where a trained model is used. Under various concept drift scenarios, the method improves performance by up to 87.3%. It is also shown that the re-training method (a representative existing method) has several limitations. This method requires several issues to be solved, such as selection of a proper window size, and this is evident through results showing different performance under different settings. In contrast, the method shows a reliable and desirable performance under various concept drift scenarios and thus outperforms the re-training method. The method improves performance by up to 79%.