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학술지 Comparative analysis of photovoltaic performance metrics for reliable performance loss rate
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저자
이현용, 이준기, 김낙우, 이병탁
발행일
202303
출처
IET Renewable Power Generation, v.17 no.4, pp.1008-1019
ISSN
1752-1416
출판사
IET
DOI
https://dx.doi.org/10.1049/rpg2.12651
협약과제
22ZK1100, 호남권 지역산업 기반 ICT 융합기술 고도화 지원사업, 강현서
초록
A reliable performance loss rate of photovoltaic systems requires accurate and reliable performance metrics. This study proposes a systematic method for assessing the performance metrics, particularly predicted power models in terms of both accuracy and uncertainty. The gist of the proposed method is to examine how accurately a predicted power model predicts the manipulated degradation in a controlled environment. For this, the proposed method divides a given dataset evenly into base data (to generate reference performance) and test data (to generate test performance via manipulation) so that the two data have similar features. The proposed method also utilizes the bootstrap iteration to derive a reliable assessment. The novelty of this study is that the proposed method estimates both the accuracy and uncertainty of arbitrary predicted power models, which is missing in existing works. Extensive experiments using the proposed method with real-world datasets reveal the followings. One interesting observation is that a well-known machine learning prediction model, not considered in existing works, exhibits the best performance in terms of both accuracy and uncertainty. Existing predicted power models require different experiment settings to produce reliable performance. The number of test data is closely related to uncertainty, but not much related to혻accuracy.
KSP 제안 키워드
Best performance, Comparative analysis, Controlled environment, Learning prediction, Performance loss rate, Photovoltaic systems(PVS), Power model, Real-world, Systematic method, Test data, Test performance
본 저작물은 크리에이티브 커먼즈 저작자 표시 - 비영리 - 변경금지 (CC BY NC ND) 조건에 따라 이용할 수 있습니다.
저작자 표시 - 비영리 - 변경금지 (CC BY NC ND)