A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trends

El-Amarty, Naima; Marzouq, Manal; El Fadili, Hakim; Bennani, Saad Dosse; Ruano, Antonio

http://hdl.handle.net/10400.1/19029

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
s11356-022-24240-w.pdf		2.33 MB	Adobe PDF	Download
s11356-023-25750-x.pdf		421.08 KB	Adobe PDF	Download

Send Feedback

Authors

Abstract(s)

Solar irradiation data are imperatively required for any solar energy-based project. The non-accessibility and uncertainty of these data can greatly affect the implementation, management, and performance of photovoltaic or thermal systems. Developing solar irradiation estimation and forecasting approaches is an effective way to overcome these issues. Practically, prediction approaches can help anticipate events by ensuring good operation of the power network and maintaining a precise balance between the demand and supply of the power at every moment. In the literature, various estimation and forecasting methods have been developed. Artificial Neural Network (ANN) models are the most commonly used methods in solar irradiation prediction. This paper aims to firstly review, analyze, and provide an overview of different aspects required to develop an ANN model for solar irradiation prediction, such as data types, data horizon, data preprocessing, forecasting horizon, feature selection, and model type. Secondly, a highly detailed state of the art of ANN-based approaches including deep learning and hybrid ANN models for solar irradiation estimation and forecasting is presented. Finally, the factors influencing prediction model performances are discussed in order to propose recommendations, trends, and outlooks for future research in this field.