Solar Power Prediction Based on Satellite Images and Support Vector Machine

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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.
Publisher
Institute of Electrical and Electronics Engineers
Issue Date
2016-07
Language
English
Article Type
Article
Keywords

RADIATION; REGRESSION; FORECAST; OUTPUT; AEROSOL; SERIES; MODEL

Citation

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, v.7, no.3, pp.1255 - 1263

ISSN
1949-3029
DOI
10.1109/TSTE.2016.2535466
URI
http://hdl.handle.net/10203/212485
Appears in Collection
EE-Journal Papers(저널논문)
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