Solar Irradiance Prediction with Machine Learning Algorithms: A Brazilian Case Study on Photovoltaic Electricity Generation

Abstract

Forecasting photovoltaic electricity generation is one of the key components to reducing the impacts of solar power natural variability, nurturing the penetration of renewable energy sources. Machine learning is a well-known method that relies on the principle that systems can learn from previously measured data, detecting patterns which are then used to predict future values of a target variable. These algorithms have been used successfully to predict incident solar irradiation, but the results depend on the specificities of the studied location due to the natural variability of the meteorological parameters. This paper presents an extensive comparison of the three ML algorithms most used worldwide for forecasting solar radiation, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Learning Machine (ELM), aiming at the best prediction of daily solar irradiance in a São Paulo context. The largest dataset in Brazil for meteorological parameters, containing measurements from 1933 to 2014, was used to train and compare the results of the algorithms. The results showed good approximation between measured and predicted global solar radiation for the three algorithms; however, for São Paulo, the SVM produced a lower Root-Mean-Square Error (RMSE), and ELM, a faster training rate. Using all 10 meteorological parameters available for the site was the best approach for the three algorithms at this location.

Artificial Intelligence-Based Deep Learning Model for the Performance Enhancement of Photovoltaic Panels in Solar Energy Systems

“…Research and development on genetic and neurological systems have seen a significant surge in recent years, particularly since the late 1980s. Recent research [15][16][17][18][19][20] has focused on the application of evolutionary algorithms to improve the operation and design of neural networks. The use of genetic algorithms, artificial neural networks, and problem-solving methodologies is also being investigated in another project.…”

“…Traditionally used methodologies, on the other hand, are unable to precisely predict the properties of solar-electricity-generating modules. Many scientists have turned to AI technologies for parameter detection as a result of this [10][11][12][13][14][15]. The synergy between artificial intelligence and other technologies can be used to build extremely powerful computer systems.…”

Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review

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Accurate Solar Radiation Forecasting using an Effective Time Series with Feature Selection

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