Short-Term Solar Power Forecasting Using SVR on Hybrid PV Power Plant in Indonesia

  • Prasetyo AjiEmail author
  • Kazumasa Wakamori
  • Hiroshi Mineno
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1035)


Considering the environmental issues, the use of renewable energy sources is a far more sustainable solution to meeting the energy demand than fossil fuels. However, the limited availability of renewable energy is a growing problem to be solved. Solar energy has become a popular renewable energy source in several countries such as Indonesia because of their equatorial locations. In this study, limited meteorological measurement has been applied with the aim of forecasting solar power generation for planning photovoltaic (PV) power plants, especially in rural areas, which have limited access to fossil energy. We used limited measurements such as temperature, humidity, and solar radiation. The use of support vector regression (SVR) was applied to improve denoising capabilities and simplify computation. SVR has been evaluated using statistical metrics such as mean absolute percentage error (MAPE), relative root means square error (NRMSE), and coefficient of determination (R2). The results showed the MAPE value obtained 18.56% from the RBF_SVR. NRMSE value performed excellently with 8.02% from the SW-SVR method. R2 also indicated good forecasting with 0.99. The results showed that promising short-term solar power generation forecasting can be applied to estimate the availability of solar power, plan for an extension, and assess the performance of hybrid power plants in Indonesia.



Prasetyo Aji was supported by Mineno Laboratory of Shizuoka University and by Research and Innovation in Science and Technology Project (RISET-PRO) World Bank Loan No. 8245-ID, Ministry of Research, Technology, and Higher Education of Indonesia. Any opinions, findings, and conclusions expressed in this material are those of the authors, and do not necessarily reflect the views of the funding agencies. Authors also would like to gratitude anonymous reviewers for their very helpful and constructive comments, which improved this manuscript from the original.


  1. 1.
    Zeng, J., Qiao, W.: Short-term solar power prediction using a support vector machine. Renew. Energy 52, 118–127 (2013)CrossRefGoogle Scholar
  2. 2.
    Li, Y., He, Y., Su, Y., Shu, L.: Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines. Appl. Energy 180, 392–401 (2016)CrossRefGoogle Scholar
  3. 3.
    Kanwal, S., Khan, B., Ali, S.M., Mehmood, C.A., Rauf, M.Q.: Support vector machine and gaussian process regression based modeling for photovoltaic power prediction. In: 2018 International Conference on Frontiers of Information Technology (FIT) (2018).
  4. 4.
    Hassan, M.Z., Ali, K.M.E., Ali, A.S., Kumar, J.: Forecasting day-ahead solar radiation using machine learning approach. In: 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (2017).
  5. 5.
    Wolff, B., Kühnert, J., Lorenz, E., Kramer, O., Heinemann, D.: Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data. Sol. Energy 135, 197–208 (2016)CrossRefGoogle Scholar
  6. 6.
    Mohammadi, K., Shamshirband, S., Anisi, M.H., Alam, K.A., Petkovic, D.: Support vector regression-based prediction of global solar radiation on a horizontal surface. Energy Convers. Manag. 91, 433–441 (2015)CrossRefGoogle Scholar
  7. 7.
    Hassan, M.A., Khalil, A., Kaseb, S., Kassem, M.A.: Potential of four different machine-learning algorithms in modeling daily global solar radiation. Renew. Energy 111, 52–62 (2017)CrossRefGoogle Scholar
  8. 8.
    Aji, P., Wakamori, K., Mineno, H.: Highly accurate daily solar radiation forecasting using SW-SVR for hybrid power plant in Indonesia. In: 2018 4th International Conference on Nano Electronics Research and Education (ICNERE) (2018).
  9. 9.
    Belaid, S., Mellit, A.: Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate. Energy Convers. Manag. 118, 105–118 (2016)CrossRefGoogle Scholar
  10. 10.
    Hackeling, G.: Mastering Machine Learning with Scikit-learn. Packt Publishing, Birmingham (2014)Google Scholar
  11. 11.
    Ahmad, M.W., Mourshed, M., Rezgui, Y.: Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression. Energy 164, 465–474 (2018)CrossRefGoogle Scholar
  12. 12.
    Lin, K.-P., Pai, P.-F.: Solar power output forecasting using evolutionary seasonal decomposition least-square support vector regression. J. Cleaner Prod. 134, 456–462 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Prasetyo Aji
    • 1
    • 2
    Email author
  • Kazumasa Wakamori
    • 1
  • Hiroshi Mineno
    • 1
  1. 1.Graduate School of Integrated Science and TechnologyShizuoka UniversityHamamatsuJapan
  2. 2.National Laboratory for Energy Conversion TechnologyAgency for the Assessment and Application of Technology (BPPT), PuspiptekTangerang SelatanIndonesia

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