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

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

kernel, photovoltaic, prediction, support vector regression

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.

Kharisma Bani Adam1
, Desri Kristina Silalahi2
, Bandiyah Sri Aprillia3
, Husayn Aththar Adhari4

Telkom University

22-08-2022

Fajar bagus W

PDF

Indonesia

Text