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Article

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

1
Facultad de Ingeniería, Institución Universitaria Pascual Bravo, 050034 Medellín, Colombia
2
Facultad de Estudios Empresariales y de Mercadeo, Institución Universitaria Esumer, 050035 Medellín, Colombia
*
Author to whom correspondence should be addressed.
Energies 2021, 14(15), 4424; https://doi.org/10.3390/en14154424
Received: 19 May 2021 / Revised: 26 June 2021 / Accepted: 30 June 2021 / Published: 22 July 2021
(This article belongs to the Section Artificial Intelligence and Smart Energy)
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. View Full-Text
Keywords: photovoltaic systems; machine learning; supervised learning; prediction; artificial neural networks; k-nearest neighbors; linear regression; support vector machine photovoltaic systems; machine learning; supervised learning; prediction; artificial neural networks; k-nearest neighbors; linear regression; support vector machine
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MDPI and ACS Style

Gutiérrez, L.; Patiño, J.; Duque-Grisales, E. A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction. Energies 2021, 14, 4424. https://doi.org/10.3390/en14154424

AMA Style

Gutiérrez L, Patiño J, Duque-Grisales E. A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction. Energies. 2021; 14(15):4424. https://doi.org/10.3390/en14154424

Chicago/Turabian Style

Gutiérrez, Leidy, Julian Patiño, and Eduardo Duque-Grisales. 2021. "A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction" Energies 14, no. 15: 4424. https://doi.org/10.3390/en14154424

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