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A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction

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  • Leidy Gutiérrez

    (Facultad de Ingeniería, Institución Universitaria Pascual Bravo, 050034 Medellín, Colombia)

  • Julian Patiño

    (Facultad de Ingeniería, Institución Universitaria Pascual Bravo, 050034 Medellín, Colombia)

  • Eduardo Duque-Grisales

    (Facultad de Ingeniería, Institución Universitaria Pascual Bravo, 050034 Medellín, Colombia
    Facultad de Estudios Empresariales y de Mercadeo, Institución Universitaria Esumer, 050035 Medellín, Colombia)

Abstract

Science seeks strategies to mitigate global warming and reduce the negative impacts of the long-term use of fossil fuels for power generation. In this sense, implementing and promoting renewable energy in different ways becomes one of the most effective solutions. The inaccuracy in the prediction of power generation from photovoltaic (PV) systems is a significant concern for the planning and operational stages of interconnected electric networks and the promotion of large-scale PV installations. This study proposes the use of Machine Learning techniques to model the photovoltaic power production for a system in Medellín, Colombia. Four forecasting models were generated from techniques compatible with Machine Learning and Artificial Intelligence methods: K-Nearest Neighbors (KNN), Linear Regression (LR), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The results obtained indicate that the four methods produced adequate estimations of photovoltaic energy generation. However, the best estimate according to RMSE and MAE is the ANN forecasting model. The proposed Machine Learning-based models were demonstrated to be practical and effective solutions to forecast PV power generation in Medellin.

Suggested Citation

  • Leidy Gutiérrez & Julian Patiño & Eduardo Duque-Grisales, 2021. "A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction," Energies, MDPI, vol. 14(15), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4424-:d:599316
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    3. Lioua Kolsi & Sameer Al-Dahidi & Souad Kamel & Walid Aich & Sahbi Boubaker & Nidhal Ben Khedher, 2022. "Prediction of Solar Energy Yield Based on Artificial Intelligence Techniques for the Ha’il Region, Saudi Arabia," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    4. 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.
    5. Tolga Yalçin & Pol Paradell Solà & Paschalia Stefanidou-Voziki & Jose Luis Domínguez-García & Tugce Demirdelen, 2023. "Exploiting Digitalization of Solar PV Plants Using Machine Learning: Digital Twin Concept for Operation," Energies, MDPI, vol. 16(13), pages 1-17, June.
    6. Wen-Chang Tsai & Chia-Sheng Tu & Chih-Ming Hong & Whei-Min Lin, 2023. "A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation," Energies, MDPI, vol. 16(14), pages 1-30, July.

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