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

Leidy Gutiérrez, Julian Patiño and Eduardo Duque-Grisales
Additional contact information
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

Energies, 2021, vol. 14, issue 15, 1-16

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.

Keywords: photovoltaic systems; machine learning; supervised learning; prediction; artificial neural networks; k-nearest neighbors; linear regression; support vector machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/15/4424/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/15/4424/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:15:p:4424-:d:599316

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().