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학술지 Model-agnostic online forecasting for PV power output
Cited 5 time in scopus Download 96 time Share share facebook twitter linkedin kakaostory
이현용, 이준기, 김낙우, 이병탁
IET Renewable Power Generation, v.15 no.15, pp.3539-3551
21PK1300, 전력 빅데이터를 활용한 신산업 BM 및 서비스 개발·검증, 이병탁
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%.
KSP 제안 키워드
Batch learning, Concept drift, Online forecasting, Online learning algorithm, Over time, Photovoltaic (PV) Power, Real-world data, Statistical properties, Window Size, forecasting method, forecasting model
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