Hourly Solar Irradiance Prediction Based on Support Vector Machine and Its Error Analysis

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Due to a variability and uncertainty of photovoltaic (PV) output power, PV operators may be subject to significant penalties on forthcoming energy markets. Thus, the accurate prediction of PV output power plays a very important role in energy market. This paper proposes a novel solar prediction scheme for one-hour ahead prediction of solar irradiance based on various meteorological factors including the cloud cover and support vector machine (SVM). A k-means clustering algorithm is applied to collect meteorological data and the entire data are classified into three clusters based on similar daily weather types. The same cluster data are used for the SVM regression in the training stage. We also investigate the prediction error analysis. It is shown that the solar irradiance prediction errors of each prediction scheme can be categorized to be leptokurtic and a t location-scale distribution is proposed as a distribution fitting for the prediction errors. In addition, the power and energy capacities of an energy storage system (ESS), which can absorb the prediction errors, are estimated from the probability density functions. Numerical results show that the proposed SVM regression scheme significantly improves the prediction accuracy and reduces the ESS installation capacity.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2017-03
Language
English
Article Type
Article
Keywords

CLUSTER-ANALYSIS; POWER OUTPUT; K-MEANS; RADIATION; SILHOUETTES; VALIDATION; ALGORITHM; FORECASTS; MODELS; SYSTEM

Citation

IEEE TRANSACTIONS ON POWER SYSTEMS, v.32, no.2, pp.935 - 945

ISSN
0885-8950
DOI
10.1109/TPWRS.2016.2569608
URI
http://hdl.handle.net/10203/223541
Appears in Collection
EE-Journal Papers(저널논문)
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