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Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning

Author

Listed:
  • Monica Borunda

    (CONACYT-Tecnológico Nacional de México -Centro Nacional de Investigación y Desarrollo Tecnológico, Cuernavaca 62490, Mexico)

  • Adrián Ramírez

    (Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico)

  • Raul Garduno

    (Instituto Nacional de Electricidad y Energias Limpias, Cuernavaca 62490, Mexico)

  • Gerardo Ruíz

    (Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico)

  • Sergio Hernandez

    (Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico)

  • O. A. Jaramillo

    (Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco 62580, Mexico)

Abstract

Solar energy currently plays a significant role in supplying clean and renewable electric energy worldwide. Harnessing solar energy through PV plants requires problems such as site selection to be solved, for which long-term solar resource assessment and photovoltaic energy forecasting are fundamental issues. This paper proposes a fast-track methodology to address these two critical requirements when exploring a vast area to locate, in a first approximation, potential sites to build PV plants. This methodology retrieves solar radiation and temperature data from free access databases for the arbitrary division of the region of interest into land cells. Data clustering and probability techniques were then used to obtain the mean daily solar radiation per month per cell, and cells are clustered by radiation level into regions with similar solar resources, mapped monthly. Simultaneously, temperature probabilities are determined per cell and mapped. Then, PV energy is calculated, including heat losses. Finally, PV energy forecasting is accomplished by constructing the P 50 and P 95 estimations of the mean yearly PV energy. A case study in Mexico fully demonstrates the methodology using hourly data from 2000 to 2020 from NSRDB. The proposed methodology is validated by comparison with actual PV plant generation throughout the country.

Suggested Citation

  • Monica Borunda & Adrián Ramírez & Raul Garduno & Gerardo Ruíz & Sergio Hernandez & O. A. Jaramillo, 2022. "Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning," Energies, MDPI, vol. 15(23), pages 1-25, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8895-:d:983428
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    References listed on IDEAS

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    Cited by:

    1. Domenico Palladino & Nicolandrea Calabrese, 2023. "Energy Planning of Renewable Energy Sources in an Italian Context: Energy Forecasting Analysis of Photovoltaic Systems in the Residential Sector," Energies, MDPI, vol. 16(7), pages 1-28, March.
    2. Tao Yan & Teng Li & Zerong Liang, 2024. "Research on the Weak-Bus Voltage Support Effect of Energy Storage Type Intelligent Soft Open Point," Energies, MDPI, vol. 17(23), pages 1-13, November.
    3. Max Olinto Moreira & Betania Mafra Kaizer & Takaaki Ohishi & Benedito Donizeti Bonatto & Antonio Carlos Zambroni de Souza & Pedro Paulo Balestrassi, 2022. "Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting," Energies, MDPI, vol. 16(1), pages 1-30, December.
    4. Juan A. Tejero-Gómez & Ángel A. Bayod-Rújula, 2023. "Analysis of Photovoltaic Plants with Battery Energy Storage Systems (PV-BESS) for Monthly Constant Power Operation," Energies, MDPI, vol. 16(13), pages 1-22, June.
    5. Hubert Kryszk & Krystyna Kurowska & Renata Marks-Bielska & Stanisław Bielski & Bartłomiej Eźlakowski, 2023. "Barriers and Prospects for the Development of Renewable Energy Sources in Poland during the Energy Crisis," Energies, MDPI, vol. 16(4), pages 1-17, February.

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