Loading...
Please wait, while we are loading the content...
Similar Documents
A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction
| Content Provider | MDPI |
|---|---|
| Author | Leidy, Gutiérrez Julian, Patiño Duque-Grisales, Eduardo |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 4424 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en14154424 |
| Journal | Energies |
| Issue Number | 15 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-07-22 |
| Access Restriction | Open |
| Subject Keyword | Energies Industrial Engineering Photovoltaic Systems Machine Learning Supervised Learning Prediction Artificial Neural Networks K-nearest Neighbors Linear Regression Support Vector Machine |
| Content Type | Text |
| Resource Type | Article |
