ETRI-Knowledge Sharing Plaform



논문 검색
구분 SCI
연도 ~ 키워드


학술대회 Bayesian Deep Learning-based Confidence-aware Solar Irradiance Forecasting System
Cited 5 time in scopus Download 0 time Share share facebook twitter linkedin kakaostory
이현용, 이병탁
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1233-1238
18ZK1100, 호남권 지역산업 기반 ICT융합기술 고도화 지원사업, 이동수
For stable and successful use of grid-connected PV (photovoltaic) plants, it is quite necessary to know the expected power from PV plants in advance. However, forecasting PV output power accurately is difficult in practical cases where uncertainties are unavoidable. In this paper, we propose a confidence-aware forecasting system that produces a point forecast together with its confidence information. Our system classifies forecast outputs into confident forecasts and non-confident forecasts using the confidence information. Then, the confident forecast is used directly and the non-confident forecast is replaced by its lower bound, which is desirable for conservative scheduling of existing power plants. Through the experiments, we show that MAPE (maximum absolute percentage error) of the confident forecasts and the non-confident forecasts are 9.8% and 21.5%, respectively. We also show that the lower bound is lower than actual value in over 95% of the non-confident forecasts. The results show that our approach is good to classify forecasts into confident forecasts and non-confident forecasts and to produce effective lower bounds.
KSP 제안 키워드
Confidence-Aware, Grid-connected PV, Learning-based, Lower bound, Output power, PV Output, PV plants, Point forecast, deep learning(DL), power plant, solar irradiance forecasting