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Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production

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  • Javier Huertas Tato

    (Department of Computer Science, Universidad Carlos III de Madrid, 28911 Madrid, Spain)

  • Miguel Centeno Brito

    (Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal)

Abstract

Solar energy forecasting is an active research problem and a key issue to increase the competitiveness of solar power plants in the energy market. However, using meteorological, production, or irradiance data from the past is not enough to produce accurate forecasts. This article aims to integrate a prediction algorithm (Smart Persistence), irradiance, and past production data, using a state-of-the-art machine learning technique (Random Forests). Three years of data from six solar PV modules at Faro (Portugal) are analyzed. A set of features that combines past data, predictions, averages, and variances is proposed for training and validation. The experimental results show that using Smart Persistence as a Machine Learning input greatly improves the accuracy of short-term forecasts, achieving an NRMSE of 0.25 on the best panels at short horizons and 0.33 on a 6 h horizon.

Suggested Citation

  • Javier Huertas Tato & Miguel Centeno Brito, 2018. "Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production," Energies, MDPI, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:100-:d:193861
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    References listed on IDEAS

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    Cited by:

    1. Mathieu Pichault & Claire Vincent & Grant Skidmore & Jason Monty, 2021. "Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR," Energies, MDPI, vol. 14(9), pages 1-21, May.
    2. Paweł Piotrowski & Mirosław Parol & Piotr Kapler & Bartosz Fetliński, 2022. "Advanced Forecasting Methods of 5-Minute Power Generation in a PV System for Microgrid Operation Control," Energies, MDPI, vol. 15(7), pages 1-23, April.
    3. Antonio Bracale & Guido Carpinelli & Pasquale De Falco, 2019. "Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method," Energies, MDPI, vol. 12(6), pages 1-16, March.
    4. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George Anders, 2023. "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation," Energies, MDPI, vol. 16(10), pages 1-24, May.
    5. Mirosław Parol & Paweł Piotrowski & Piotr Kapler & Mariusz Piotrowski, 2021. "Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control," Energies, MDPI, vol. 14(5), pages 1-29, February.

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