ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술지 Confidence-aware Deep Learning Forecasting System for Daily Solar Irradiance
Cited 5 time in scopus Download 4 time Share share facebook twitter linkedin kakaostory
저자
이현용, 이병탁
발행일
201907
출처
IET Renewable Power Generation, v.13 no.10, pp.1681-1689
ISSN
1752-1416
출판사
IET
DOI
https://dx.doi.org/10.1049/iet-rpg.2018.5354
협약과제
19ZK1100, 호남권 지역산업 기반 ICT융합기술 고도화 지원사업, 이길행
초록
For better deep learning forecasting systems for photovoltaic systems, confidence information about a point forecast is necessary in practical cases where uncertainties are unavoidable. In this study, using Bayesian deep learning, the authors introduce a confidence-aware deep learning forecasting system that provides confidence information as well as a point forecast. Through the experiments using the real-world data, they first solve three main issues caused by when Bayesian deep learning is applied to the forecasting of daily solar irradiance using weather forecast: selection of neural network model, selection of validation data to be used for estimating the confidence information, and ways for estimating the confidence information. Then, they examine the feasibility of the confidence-aware deep learning forecasting system in estimating the confidence information. From the experiments, classifying the forecast outputs into confident outputs and non-confident outputs using the confidence information, they show that maximum absolute percentage error of confident forecast outputs and non-confident forecast outputs are 5 and 22.8% at a specific classification threshold, respectively. This result shows that their confidence-aware deep learning forecasting system is good to estimate meaningful confidence information that is closely related to the forecast accuracy.
KSP 제안 키워드
Confidence-Aware, Forecast Accuracy, Photovoltaic systems(PVS), Point forecast, Real-world data, Solar irradiance, Validation data, Weather Forecast, deep learning(DL), neural network model