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http://dx.doi.org/10.12673/jant.2017.21.6.643

Photovoltaic Generation Forecasting Using Weather Forecast and Predictive Sunshine and Radiation  

Shin, Dong-Ha (Department of Energy IT, Gachon University)
Park, Jun-Ho (Department of Energy IT, Gachon University)
Kim, Chang-Bok (Department of Energy IT, Gachon University)
Publication Information
Journal of Advanced Navigation Technology / v.21, no.6, 2017 , pp. 643-650 More about this Journal
Abstract
Photovoltaic generation which has unlimited energy sources are very intermittent because they depend on the weather. Therefore, it is necessary to get accurate generation prediction with reducing the uncertainty of photovoltaic generation and improvement of the economics. The Meteorological Agency predicts weather factors for three days, but doesn't predict the sunshine and solar radiation that are most correlated with the prediction of photovoltaic generation. In this study, we predict sunshine and solar radiation using weather, precipitation, wind direction, wind speed, humidity, and cloudiness which is forecasted for three days at Meteorological Agency. The photovoltaic generation forecasting model is proposed by using predicted solar radiation and sunshine. As a result, the proposed model showed better results in the error rate indexes such as MAE, RMSE, and MAPE than the model that predicts photovoltaic generation without radiation and sunshine. In addition, DNN showed a lower error rate index than using SVM, which is a type of machine learning.
Keywords
Solar photovoltaic generation forecast; Deep learning; Artificial neural network; Support vector machine;
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Times Cited By KSCI : 6  (Citation Analysis)
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