LAPSE:2023.25383
Published Article
LAPSE:2023.25383
A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction
March 28, 2023
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.
Keywords
artificial neural networks, k-nearest neighbors, linear regression, Machine Learning, photovoltaic systems, prediction, supervised learning, support vector machine
Suggested Citation
Gutiérrez L, Patiño J, Duque-Grisales E. A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction. (2023). LAPSE:2023.25383
Author Affiliations
Gutiérrez L: Facultad de Ingeniería, Institución Universitaria Pascual Bravo, 050034 Medellín, Colombia
Patiño J: Facultad de Ingeniería, Institución Universitaria Pascual Bravo, 050034 Medellín, Colombia [ORCID]
Duque-Grisales E: Facultad de Ingeniería, Institución Universitaria Pascual Bravo, 050034 Medellín, Colombia; Facultad de Estudios Empresariales y de Mercadeo, Institución Universitaria Esumer, 050035 Medellín, Colombia [ORCID]
Journal Name
Energies
Volume
14
Issue
15
First Page
4424
Year
2021
Publication Date
2021-07-22
Published Version
ISSN
1996-1073
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Original Submission
Other Meta
PII: en14154424, Publication Type: Journal Article
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LAPSE:2023.25383
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doi:10.3390/en14154424
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