| Title |
A Study on the Development of the short-term Photovoltaic Power Forecasting System using Support Vector Regression (SVR) |
| DOI |
http://dx.doi.org/10.5207/JIEIE.2019.33.6.042 |
| Keywords |
Photovoltaic Power Forecasting ; Support Vector Regression(SVR) ; Day-ahead Forecasting ; Short-Term Forecasting ; Machine Learning |
| Abstract |
Uncertainty and variability in photovoltaic power generation can cause instability in the power system. Accurate photovoltaic power generation forecasts are needed to reduce instability in the system of solar power generation. Forecasting is the most cost-effective method of improving the reliability of the system. In this paper, we propose the short-term photovoltaic power forecasting using support vector regression. Kriging method is used to estimate the solar irradiance of the solar plant. When forecasting solar power through the support vector regression model, the accuracy of the forecasting was high compared to other models. |