Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term
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 referen...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2022-08-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4134 |
| _version_ | 1797329588065402880 |
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| author | Kharisma Bani Adam Desri Kristina Silalahi Bandiyah Sri Aprillia Husayn Aththar Adhari |
| author_facet | Kharisma Bani Adam Desri Kristina Silalahi Bandiyah Sri Aprillia Husayn Aththar Adhari |
| author_sort | Kharisma Bani Adam |
| collection | DOAJ |
| description | 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 |
| first_indexed | 2024-03-08T07:06:41Z |
| format | Article |
| id | doaj.art-63b50e7e84124b8597f094944b211843 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T07:06:41Z |
| publishDate | 2022-08-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-63b50e7e84124b8597f094944b2118432024-02-03T04:17:44ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602022-08-016454855410.29207/resti.v6i4.41344134Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short TermKharisma Bani Adam0Desri Kristina Silalahi1Bandiyah Sri Aprillia2Husayn Aththar Adhari3Telkom University Telkom University TELKOM UNIVERSITYTelkom UniversityPhotovoltaic (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 outputhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/4134kernel, photovoltaic, prediction, support vector regression |
| spellingShingle | Kharisma Bani Adam Desri Kristina Silalahi Bandiyah Sri Aprillia Husayn Aththar Adhari Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) kernel, photovoltaic, prediction, support vector regression |
| title | Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term |
| title_full | Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term |
| title_fullStr | Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term |
| title_full_unstemmed | Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term |
| title_short | Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term |
| title_sort | support vector regression method for predicting off grid photovoltaic output power in the short term |
| topic | kernel, photovoltaic, prediction, support vector regression |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4134 |
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