Journal
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 7, Issue 3, Pages 1255-1263Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2016.2535466
Keywords
Solar power; Irradiance; Prediction; Forecasting; Photovoltaic; Satellite images; Support vector machine; Machine learning
Categories
Funding
- Climate Change Research Hub Project of the KAIST EEWS Research Center [EEWS-2016-N11160018]
- KUSTAR-KAIST Institute, under the RD program
- Ministry of Science, ICT & Future Planning, Republic of Korea [N11160027] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Penetration of solar energy into main grid has gradually increased in recent years due to a growing number of large-scale photovoltaic (PV) farms. The power output of these PV farms may fluctuate due to a wide variability of meteorological conditions, and, thus, we need to compensate for this effect in advance. In this paper, we propose a solar power prediction model based on various satellite images and a support vector machine (SVM) learning scheme. The motion vectors of clouds are forecasted by utilizing satellite images of atmospheric motion vectors (AMVs). We analyze 4 years' historical satellite images and utilize them to configure a large number of input and output data sets for the SVM learning. We compare the performance of the proposed SVM-based model, the conventional time-series model, and an artificial neural network (ANN) model in terms of prediction accuracy.
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