Solar Irradiation Forecasting using Genetic Algorithms

Renewable energy forecasting is attaining greater importance due to its constant increase in contribution to the electrical power grids. Solar energy is one of the most significant contributors to renewable energy and is dependent on solar irradiation. For the effective management of electrical powe...

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Published in:arXiv.org 2021-06
Main Authors: Gunasekaran, V, Kovi, K K, Arja, S, Chimata, R
Format: Article
Language:English
Subjects:
Alternative energy sources
Electric power grids
Forecasting
Genetic algorithms
Irradiation
Machine learning
Mathematical models
Optimization
Photovoltaic cells
Renewable energy
Renewable resources
Solar energy
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creator Gunasekaran, V
Kovi, K K
Arja, S
Chimata, R
description Renewable energy forecasting is attaining greater importance due to its constant increase in contribution to the electrical power grids. Solar energy is one of the most significant contributors to renewable energy and is dependent on solar irradiation. For the effective management of electrical power grids, forecasting models that predict solar irradiation, with high accuracy, are needed. In the current study, Machine Learning techniques such as Linear Regression, Extreme Gradient Boosting and Genetic Algorithm Optimization are used to forecast solar irradiation. The data used for training and validation is recorded from across three different geographical stations in the United States that are part of the SURFRAD network. A Global Horizontal Index (GHI) is predicted for the models built and compared. Genetic Algorithm Optimization is applied to XGB to further improve the accuracy of solar irradiation prediction.
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subjects Alternative energy sources
Electric power grids
Forecasting
Genetic algorithms
Irradiation
Machine learning
Mathematical models
Optimization
Photovoltaic cells
Renewable energy
Renewable resources
Solar energy
title Solar Irradiation Forecasting using Genetic Algorithms
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