Advance Techniques for Solar Radiation, Wind Speed and Photovoltaic Forecasting

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Power and Energy Forecasting".

Deadline for manuscript submissions: 11 June 2024 | Viewed by 2655

Special Issue Editors

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Guest Editor
Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaïb Doukkali University of El Jadida, El Jadida M-24000, Morocco
Interests: performance analysis; monitoring; lifetime analysis; fault detection; control management; power electronics; hybrid renewable energy; mathematical modelling; optimization and meta-heuristic algorithm; computational intelligence; photovoltaic and power energy; forecasting; fuel cell; radar; radio frequency; electromagnetic and electronic
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Energy, Politecnico di Milano, 20156 Milan, Italy
Interests: photovoltaic system; grid; power sharing; inverters; forecasting; nowcasting; machine learning; degradation; battery management systems; polymer solar cells; organic photovoltaics; electric vehicle; vehicle-to-grid; microgrid; energy systems; maximum power point trackers; electric power plant loads; electricity price; power markets; heterogeneous networks; base stations; energy efficiency; life cycle assessment; wind power; regenerative braking; bicycles; motorcycles; car sharing; autonomous vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advance techniques and methods such as machine learning and artificial intelligence are essential parts of interdisciplinary operational research. Various techniques based on advanced methods have been used in the study of solar radiation, wind turbines and photovoltaic power forecasting. The used technique is based on the weather conditions and the physical and mathematical models. The forecasting techniques are divided into three categories: physical models, statistical models and hybrid models. The aim of this Special Issue is to forecast the solar radiation, wind speed and photovoltaic power output using advance techniques, and to publish high quality papers on these subjects. The forecasted data can be used for the smart grid to ensure its stability or to forecast the output of the smart grid. The purpose of this Special Issue is to encourage scholars and practitioners to publish and discuss novel and high-quality papers.

This Special Issue is devoted to modelling, forecasting, optimization and application in the broadest sense, covering recent conceptual results and the implementations of newly created techniques to study solar radiation and solar cells/photovoltaics.

We welcome both original research and review articles. Potential topics include, but are not limited to, the following:

  • Forecasting of photovoltaic/thermal
  • Solar radiation forecasting
  • Wind speed and wind turbine forecasting
  • Photovoltaic power output and temperature forecasting
  • Forecasting and modelling of reliability and lifetime of photovoltaics
  • Forecasting of photovoltaic systems characterization
  • Solar cell and photovoltaic parameters extraction, identification and forecasting
  • Performance modelling and analysis of photovoltaic/thermal and solar radiation forecasting
  • Photovoltaic MPPT optimization and forecasting
  • Forecasting of photovoltaic/wind turbine forecasting

Dr. Mohamed Louzazni
Prof. Dr. Sonia Leva
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • forecasting techniques
  • machine learning
  • computational intelligence
  • solar radiation forecasting
  • wind speed forecasting
  • photovoltaic output forecasting

Published Papers (1 paper)

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24 pages, 4421 KiB  
Global Solar Radiation Forecasting Based on Hybrid Model with Combinations of Meteorological Parameters: Morocco Case Study
by Brahim Belmahdi, Mohamed Louzazni, Mousa Marzband and Abdelmajid El Bouardi
Forecasting 2023, 5(1), 172-195; - 27 Jan 2023
Cited by 2 | Viewed by 1795
The adequate modeling and estimation of solar radiation plays a vital role in designing solar energy applications. In fact, unnecessary environmental changes result in several problems with the components of solar photovoltaic and affects the energy generation network. Various computational algorithms have been [...] Read more.
The adequate modeling and estimation of solar radiation plays a vital role in designing solar energy applications. In fact, unnecessary environmental changes result in several problems with the components of solar photovoltaic and affects the energy generation network. Various computational algorithms have been developed over the past decades to improve the efficiency of predicting solar radiation with various input characteristics. This research provides five approaches for forecasting daily global solar radiation (GSR) in two Moroccan cities, Tetouan and Tangier. In this regard, autoregressive integrated moving average (ARIMA), autoregressive moving average (ARMA), feed forward back propagation neural networks (FFBP), hybrid ARIMA-FFBP, and hybrid ARMA-FFBP were selected to compare and forecast the daily global solar radiation with different combinations of meteorological parameters. In addition, the performance in three approaches has been calculated in terms of the statistical metric correlation coefficient (R2), root means square error (RMSE), stand deviation (σ), the slope of best fit (SBF), legate’s coefficient of efficiency (LCE), and Wilmott’s index of agreement (WIA). The best model is selected by using the computed statistical metric, which is present, and the optimal value. The R2 of the forecasted ARIMA, ARMA, FFBP, hybrid ARIMA-FFBP, and ARMA-FFBP models is varying between 0.9472% and 0.9931%. The range value of SPE is varying between 0.8435 and 0.9296. The range value of LCE is 0.8954 and 0.9696 and the range value of WIA is 0.9491 and 0.9945. The outcomes show that the hybrid ARIMA–FFBP and hybrid ARMA–FFBP techniques are more effective than other approaches due to the improved correlation coefficient (R2). Full article
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