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학술지 Uncertainty-aware Forecast Interval for Hourly PV Power Output
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저자
이현용, 김낙우, 이준기, 이병탁
발행일
201910
출처
IET Renewable Power Generation, v.13 no.14, pp.2656-2664
ISSN
1752-1416
출판사
IET
DOI
https://dx.doi.org/10.1049/iet-rpg.2019.0300
협약과제
19ZK1100, 호남권 지역산업 기반 ICT융합기술 고도화 지원사업, 이길행
초록
A forecast interval is effective for handling the forecast uncertainty in solar photovoltaic systems. In estimating the forecast interval, most available approaches apply an identical policy to all the point forecasts. This results in an inefficient interval (e.g. an unnecessarily wide interval for an accurate forecast). They also adopt a complex model and even require modification of the available deterministic forecasting model, which may adversely affect their application. To overcome these limitations, the authors introduce a forecast uncertainty-aware forecast interval. They calculate a forecast accuracy-related uncertainty metric from an ensemble method based on the dropout technique. The dropout technique is widely used in deep learning models. This implies that the proposed approach can be applied to available deep learning forecasting models without modifying them. Using the uncertainty metric and relevant data of previous forecast results, they estimate the uncertainty-aware forecast interval. Through experiments using real-world data, they first demonstrate the close relation of their uncertainty metric to the forecasting accuracy. Then, they demonstrate that the uncertainty-aware forecast interval reduces the mean interval length by up to 25.7% and decreases the prediction interval coverage probability by 4.07%, compared to available approaches. This illustrates that their approach results in an effective interval.
KSP 제안 키워드
Complex models, Coverage probability, Ensemble method, Forecast Accuracy, Forecast uncertainty, PV power, Photovoltaic systems(PVS), Prediction interval, Real-world data, Relevant data, Solar photovoltaic(SPV)