Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term

Abstract

Photovoltaic (PV) technology is a renewable technology utilizing conversion of solar power or solar radiation into electrical energy. In the manufacture of Solar Power Generation systems, reference is needed regarding the cost of generation and scheduling of maintenance plans. To obtain this reference, it is necessary to predict the photovoltaic power output which is used to determine the power output of PV in the future. In this study, a system that is used to predict short-term power output in PV is designed. This system uses solar irradiation data and 42 days of power output in off-grid PV mini-grid as the dataset. The dataset obtained from the PV output is processed using the Support Vector Regression method with the Kernel Radial Basis Function (RBF) function. Based on the dataset used, this study succeeded in testing the best kernel, namely the RBF kernel. Evaluation of the prediction model obtained a smaller error value than other kernel tests with a Mean Absolute Percentage Error (MAPE) value of 21.082%, Mean Square Error (MSE) value of 0.122, and Mean Absolute Error (MAE) value of 0.262. The prediction model obtained is used to predict the short-term PV power output for the next 3 days. The results of the prediction model have an error value of 5.785 % for MAPE, 0.005 for MAE and 0.069 for MSE. Therefore, the predictive model can be categorized as very good and feasible to predict short-term power output

Forecasting Photovoltaic Output Power Based on Environmental Parameters Using Artificial Neural Network Methods

“…The Support Vector Regression method with the Kernel RBF function can predict the photovoltaic output power with MAPE values in the range of 20%. MAE value is 0.004 and MSE is 0.069 [12]. The evaluation results of the forecasting model can predict the photovoltaic output power well.…”