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Short Term Prediction of PV Power Output Generation Using Hierarchical Probabilistic Model

Author

Listed:
  • Dongkyu Lee

    (Department of Architectural Engineering, Hanyang University, 222 Wangsimni-Ro, Seungdong-Gu, Seoul 04763, Korea)

  • Jae-Weon Jeong

    (Department of Architectural Engineering, Hanyang University, 222 Wangsimni-Ro, Seungdong-Gu, Seoul 04763, Korea)

  • Guebin Choi

    (Department of Statistics (Institute of Applied Statistics), Jeonbuk National University, Jeonju 54896, Korea)

Abstract

Photovoltaics are methods used to generate electricity by using solar cells, which convert natural energy from the sun. This generation makes use of unlimited natural energy. However, this generation is irregular because they depend on weather occurrences. For this reason, there is a need to improve their economic efficiency through accurate predictions and reducing their uncertainty. Most researches were conducted to predict photovoltaic generation with various machine learning and deep learning methods that have complicated structures and over-fitted performances. As improving the performance, this paper explores the probabilistic approach to improve the prediction of the photovoltaic rate of power output per hour. This research conducted a variable correlation analysis with output values and a specific EM algorithm (expectation and maximization) made from 6054 observations. A comparison was made between the performance of the EM algorithm with five different machine learning algorithms. The EM algorithm exhibited the best performance compared to other algorithms with an average of 0.75 accuracies. Notably, there is the benefit of performance, stability, the goodness of fit, lightness, and avoiding overfitting issues using the EM algorithm. According to the results, the EM algorithm improves photovoltaic power output prediction with simple weather forecasting services.

Suggested Citation

  • Dongkyu Lee & Jae-Weon Jeong & Guebin Choi, 2021. "Short Term Prediction of PV Power Output Generation Using Hierarchical Probabilistic Model," Energies, MDPI, vol. 14(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2822-:d:554621
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    References listed on IDEAS

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    Cited by:

    1. Wenying Li & Ming Tang & Xinzhen Zhang & Danhui Gao & Jian Wang, 2022. "Optimal Operation for Regional IES Considering the Demand- and Supply-Side Characteristics," Energies, MDPI, vol. 15(4), pages 1-27, February.
    2. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    3. Zoltan Varga & Ervin Racz, 2022. "Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System," Energies, MDPI, vol. 15(19), pages 1-18, October.

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