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Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models

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  • Seungbeom Nam

    (Department of Electrical Engineering, Sangmyung University, Seoul 03016, Korea)

  • Jin Hur

    (Department of Electrical Engineering, Sangmyung University, Seoul 03016, Korea)

Abstract

Solar power’s variability makes managing power system planning and operation difficult. Facilitating a high level of integration of solar power resources into a grid requires maintaining the fundamental power system so that it is stable when interconnected. Accurate and reliable forecasting helps to maintain the system safely given large-scale solar power resources; this paper therefore proposes a probabilistic forecasting approach to solar resources using the R statistics program, applying a hybrid model that considers spatio-temporal peculiarities. Information on how the weather varies at sites of interest is often unavailable, so we use a spatial modeling procedure called kriging to estimate precise data at the solar power plants. The kriging method implements interpolation with geographical property data. In this paper, we perform day-ahead forecasts of solar power based on the probability in one-hour intervals by using a Naïve Bayes Classifier model, which is a classification algorithm. We augment forecasting by taking into account the overall data distribution and applying the Gaussian probability distribution. To validate the proposed hybrid forecasting model, we perform a comparison of the proposed model with a persistence model using the normalized mean absolute error (NMAE). Furthermore, we use empirical data from South Korea’s meteorological towers (MET) to interpolate weather variables at points of interest.

Suggested Citation

  • Seungbeom Nam & Jin Hur, 2018. "Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models," Energies, MDPI, vol. 11(11), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2982-:d:179790
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    References listed on IDEAS

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    1. Philippe Lauret & Mathieu David & Hugo T. C. Pedro, 2017. "Probabilistic Solar Forecasting Using Quantile Regression Models," Energies, MDPI, vol. 10(10), pages 1-17, October.
    2. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    3. Antonio Bracale & Pierluigi Caramia & Guido Carpinelli & Anna Rita Di Fazio & Gabriella Ferruzzi, 2013. "A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control," Energies, MDPI, vol. 6(2), pages 1-15, February.
    4. Azhar Ahmed Mohammed & Zeyar Aung, 2016. "Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation," Energies, MDPI, vol. 9(12), pages 1-17, December.
    5. Luca Massidda & Marino Marrocu, 2018. "Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting," Energies, MDPI, vol. 11(7), pages 1-20, July.
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    Cited by:

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    2. Happy Aprillia & Hong-Tzer Yang & Chao-Ming Huang, 2020. "Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm," Energies, MDPI, vol. 13(8), pages 1-20, April.
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    4. Chu, Yinghao & Wang, Yiling & Yang, Dazhi & Chen, Shanlin & Li, Mengying, 2024. "A review of distributed solar forecasting with remote sensing and deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
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    6. Yunus Yalman & Tayfun Uyanık & İbrahim Atlı & Adnan Tan & Kamil Çağatay Bayındır & Ömer Karal & Saeed Golestan & Josep M. Guerrero, 2022. "Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid," Energies, MDPI, vol. 15(18), pages 1-16, September.
    7. Cheng Yan & Jianfeng Zhu & Xiuli Shen & Jun Fan & Dong Mi & Zhengming Qian, 2020. "Ensemble of Regression-Type and Interpolation-Type Metamodels," Energies, MDPI, vol. 13(3), pages 1-20, February.
    8. Kihan Kim & Jin Hur, 2019. "Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources," Energies, MDPI, vol. 12(17), pages 1-13, August.

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