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A Hybrid Technique for Day-Ahead PV Generation Forecasting Using Clear-Sky Models or Ensemble of Artificial Neural Networks According to a Decision Tree Approach

Author

Listed:
  • Stefano Massucco

    (Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy)

  • Gabriele Mosaico

    (Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy)

  • Matteo Saviozzi

    (Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy)

  • Federico Silvestro

    (Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy)

Abstract

PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results.

Suggested Citation

  • Stefano Massucco & Gabriele Mosaico & Matteo Saviozzi & Federico Silvestro, 2019. "A Hybrid Technique for Day-Ahead PV Generation Forecasting Using Clear-Sky Models or Ensemble of Artificial Neural Networks According to a Decision Tree Approach," Energies, MDPI, vol. 12(7), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1298-:d:220020
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    References listed on IDEAS

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    5. Stefano Bianchi & Allegra De Filippo & Sandro Magnani & Gabriele Mosaico & Federico Silvestro, 2021. "VIRTUS Project: A Scalable Aggregation Platform for the Intelligent Virtual Management of Distributed Energy Resources," Energies, MDPI, vol. 14(12), pages 1-31, June.
    6. Cai Tao & Junjie Lu & Jianxun Lang & Xiaosheng Peng & Kai Cheng & Shanxu Duan, 2021. "Short-Term Forecasting of Photovoltaic Power Generation Based on Feature Selection and Bias Compensation–LSTM Network," Energies, MDPI, vol. 14(11), pages 1-16, May.
    7. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    8. Guilherme Henrique Alves & Geraldo Caixeta Guimarães & Fabricio Augusto Matheus Moura, 2023. "Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing," Energies, MDPI, vol. 16(14), pages 1-30, July.
    9. Thi Ngoc Nguyen & Felix Musgens, 2021. "What drives the accuracy of PV output forecasts?," Papers 2111.02092, arXiv.org.
    10. Thomas Price & Gordon Parker & Gail Vaucher & Robert Jane & Morris Berman, 2022. "Microgrid Energy Management during High-Stress Operation," Energies, MDPI, vol. 15(18), pages 1-11, September.
    11. Wei Li & Hui Ren & Ping Chen & Yanyang Wang & Hailong Qi, 2020. "Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review," Energies, MDPI, vol. 13(22), pages 1-25, November.

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