Machine learning methods for solar radiation forecasting: A review

By Cyril Voyant, G. Notton, Soteris A. Kalogirou, Marie-Laure Nivet, Christophe Paoli, F. Motte, A. Fouilloy. 2017

1,563 citations recorded by the source corpus.

Forecasting the output power of solar systems is required for the good operation of the power grid or for the optimal management of the energy fluxes occurring into the solar system. Before forecasting the solar systems output, it is essential to focus the prediction on the solar irradiance. The global solar radiation forecasting can be performed by several methods; the two big categories are the cloud imagery combined with physical models, and the machine learning models. In this context, the objective of this paper is to give an overview of forecasting methods of solar irradiation using machine learning approaches. Although, a lot of papers describes methodologies like neural networks or support vector regression, it will be shown that other methods (regression tree, random forest, gradient boosting and many others) begin to be used in this context of prediction. The performance ranking of such methods is complicated due to the diversity of the data set, time step, forecasting horizon, set up and performance indicators. Overall, the error of prediction is quite equivalent. To improve the prediction performance some authors proposed the use of hybrid models or to use an ensemble forecast approach.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

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Citations in the corpus

Estimating surface solar irradiance from satellites: Past, present, and future perspectives (2019, background): “, 2008) that have recently been reviewed (Voyant et al., 2017; Zhang et al., 2017).”
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, methodology): “The ability to develop a relationship between inputs and outputs, even when their representation is impossible, makes these models suitable for pattern recognition, classification, data mining and forecasting [18].”
Tackling Climate Change with Machine Learning (2019, background): “[34] Cyril Voyant, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, and Alexis Fouilloy.”
Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, background): “Prediction models for both meteorological forecasts and system outputs were presented in [172]–[178].”
Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches (2018, background): “For example, machine learning and AI have been used extensively to forecast the power generated from solar panels and wind turbines [6].”