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

  • Kharisma Bani Adam Telkom University
  • Desri Kristina Silalahi Telkom University
  • Bandiyah Sri Aprillia TELKOM UNIVERSITY
  • Husayn Aththar Adhari Telkom University
Keywords: 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


Download data is not yet available.


M. H. Ahmadi et al., “Solar power technology for electricity generation: A critical review,” Energy Science and Engineering, vol. 6, no. 5. 2018, doi: 10.1002/ese3.239.

The International Renewable Energy Agency, “Renewable Energy Statistics 2021,” 2021.

IEA, “Renewable Energy Market Update, Outlook for 2020 and 2021,” Renew. Energy Mark. Updat., 2020.

ADB, “Energy Sector Assessment, Strategy, and Road Map: Indonesia,” 2020.

M. N. Akhter, S. Mekhilef, H. Mokhlis, and N. M. Shah, “Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques,” IET Renewable Power Generation, vol. 13, no. 7. 2019, doi: 10.1049/iet-rpg.2018.5649.

L. Liu, D. Liu, Q. Sun, H. Li, and R. Wennersten, “Forecasting Power Output of Photovoltaic System Using A BP Network Method,” in Energy Procedia, 2017, vol. 142, doi: 10.1016/j.egypro.2017.12.126.

S. Preda, S. V. Oprea, A. Bâra, and A. Belciu, “PV forecasting using support vector machine learning in a big data analytics context,” Symmetry (Basel)., vol. 10, no. 12, 2018, doi: 10.3390/sym10120748.

J. Lin and H. Li, “A Short-Term PV Power Forecasting Method Using a Hybrid Kmeans-GRA-SVR Model under Ideal Weather Condition,” J. Comput. Commun., vol. 08, no. 11, 2020, doi: 10.4236/jcc.2020.811008.

L. Cao, “Support vector machines experts for time series forecasting,” Neurocomputing, vol. 51, 2003, doi: 10.1016/S0925-2312(02)00577-5.

C. Voyant et al., “Machine learning methods for solar radiation forecasting: A review,” Renewable Energy, vol. 105. 2017, doi: 10.1016/j.renene.2016.12.095.

M. G. M. Abdolrasol et al., “Artificial neural networks based optimization techniques: A review,” Electronics (Switzerland), vol. 10, no. 21. 2021, doi: 10.3390/electronics10212689.

A. S. Wicaksono and A. A. Supianto, “Hyper parameter optimization using genetic algorithm on machine learning methods for online news popularity prediction,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 12, 2018, doi: 10.14569/IJACSA.2018.091238.

D. Van Dao, H. B. Ly, S. H. Trinh, T. T. Le, and B. T. Pham, “Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete,” Materials (Basel)., vol. 12, no. 6, 2019, doi: 10.3390/ma12060983.

H. Zhang, L. Chen, Y. Qu, G. Zhao, and Z. Guo, “Support vector regression based on grid-search method for short-term wind power forecasting,” J. Appl. Math., vol. 2014, 2014, doi: 10.1155/2014/835791.

A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, vol. 14, no. 3. 2004, doi: 10.1023/B:STCO.0000035301.49549.88.

A. Lindholm, N. Wahlström, F. Lindsten, and T. B. Schön, Machine Learning A First Course for Engineers and Scientists. Inggris: Cambridge University Press, 2022.

N. Ibrahim and A. Wibowo, “Support vector regression with missing data treatment based variables selection for water level prediction of galas river in Kelantan Malaysia,” WSEAS Trans. Math., vol. 13, 2014.

C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer-Verlag New York Inc., 2006.

and C.-J. L. Chih-Wei Hsu, Chih-Chung Chang, “A Practical Guide to Support Vector Classification,” BJU Int., vol. 101, no. 1, 2008.

P. J. Bickel and K. A. Doksum, Mathematical statistics: basic ideas and selected topics, volume i, second edition. USA: CRC Press, 2015.

D. W. Stroock, Probability theory : an analytic view. New York: Cambridge, 2011.

S. Kim and H. Kim, “A new metric of absolute percentage error for intermittent demand forecasts,” Int. J. Forecast., vol. 32, no. 3, pp. 669–679, 2016, doi:

P.-C. Chang, Y.-W. Wang, and C.-H. Liu, “The development of a weighted evolving fuzzy neural network for PCB sales forecasting,” Expert Syst. Appl., vol. 32, pp. 86–96, 2007, doi: doi:10.1016/j.eswa.2005.11.021.

D. Kristina Silalahi, H. Murfi, and Y. Satria, “Studi Perbandingan Pemilihan Fitur untuk Support Vector Machine pada Klasifikasi Penilaian Risiko Kredit,” 2017.

F. Zhou, “Cross-validation research based on RBF-SVR model for stock index prediction,” Data Sci. Financ. Econ., vol. 1, no. 1, pp. 1–20, 2021.

How to Cite
Kharisma Bani Adam, Desri Kristina Silalahi, Bandiyah Sri Aprillia, & Husayn Aththar Adhari. (2022). Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term . Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(4), 548 - 554.
Artikel Rekayasa Sistem Informasi