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Forecasting
Special Issues
Advance Techniques for Solar Radiation, Wind Speed and Photovoltaic Forecasting
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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: 31 March 2025 | Viewed by 7570

Special Issue Editors

Dr. Mohamed Louzazni

E-Mail Website
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
Prof. Dr. Sonia Leva

E-Mail Website1 Website2
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

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.

Keywords

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

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

23 pages, 1046 KiB  
Article
Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory
by Kgothatso Makubyane and Daniel Maposa
Forecasting 2024, 6(4), 885-907; https://doi.org/10.3390/forecast6040044 - 4 Oct 2024
Viewed by 1135
Abstract
This study investigates wind speed prediction using advanced machine learning techniques, comparing the performance of Vanilla long short-term memory (LSTM) and convolutional neural network (CNN) models, alongside the application of extreme value theory (EVT) using the r-largest order generalised extreme value distribution ( [...] Read more.
This study investigates wind speed prediction using advanced machine learning techniques, comparing the performance of Vanilla long short-term memory (LSTM) and convolutional neural network (CNN) models, alongside the application of extreme value theory (EVT) using the r-largest order generalised extreme value distribution (GEVDr). Over the past couple of decades, the academic literature has transitioned from conventional statistical time series models to embracing EVT and machine learning algorithms for the modelling of environmental variables. This study adds value to the literature and knowledge of modelling wind speed using both EVT and machine learning. The primary aim of this study is to forecast wind speed in the Limpopo province of South Africa to showcase the dependability and potential of wind power generation. The application of CNN showcased considerable predictive accuracy compared to the Vanilla LSTM, achieving 88.66% accuracy with monthly time steps. The CNN predictions for the next five years, in m/s, were 9.91 (2024), 7.64 (2025), 7.81 (2026), 7.13 (2027), and 9.59 (2028), slightly outperforming the Vanilla LSTM, which predicted 9.43 (2024), 7.75 (2025), 7.85 (2026), 6.87 (2027), and 9.43 (2028). This highlights CNN’s superior ability to capture complex patterns in wind speed dynamics over time. Concurrently, the analysis of the GEVDr across various order statistics identified GEVDr=2 as the optimal model, supported by its favourable evaluation metrics in terms of Akaike information criteria (AIC) and Bayesian information criteria (BIC). The 300-year return level for GEVDr=2 was found to be 22.89 m/s, indicating a rare wind speed event. Seasonal wind speed analysis revealed distinct patterns, with winter emerging as the most efficient season for wind, featuring a median wind speed of 7.96 m/s. Future research could focus on enhancing prediction accuracy through hybrid algorithms and incorporating additional meteorological variables. To the best of our knowledge, this is the first study to successfully combine EVT and machine learning for short- and long-term wind speed forecasting, providing a novel framework for reliable wind energy planning. Full article
(This article belongs to the Special Issue Advance Techniques for Solar Radiation, Wind Speed and Photovoltaic Forecasting)
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28 pages, 1606 KiB  
Article
Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions
by Fhulufhelo Walter Mugware, Caston Sigauke and Thakhani Ravele
Forecasting 2024, 6(3), 672-699; https://doi.org/10.3390/forecast6030035 - 19 Aug 2024
Cited by 1 | Viewed by 1502
Abstract
The main source of electricity worldwide stems from fossil fuels, contributing to air pollution, global warming, and associated adverse effects. This study explores wind energy as a potential alternative. Nevertheless, the variable nature of wind introduces uncertainty in its reliability. Thus, it is [...] Read more.
The main source of electricity worldwide stems from fossil fuels, contributing to air pollution, global warming, and associated adverse effects. This study explores wind energy as a potential alternative. Nevertheless, the variable nature of wind introduces uncertainty in its reliability. Thus, it is necessary to identify an appropriate machine learning model capable of reliably forecasting wind speed under various environmental conditions. This research compares the effectiveness of Dynamic Architecture for Artificial Neural Networks (DAN2), convolutional neural networks (CNN), random forest and XGBOOST in predicting wind speed across three locations in South Africa, characterised by different weather patterns. The forecasts from the four models were then combined using quantile regression averaging models, generalised additive quantile regression (GAQR) and quantile regression neural networks (QRNN). Empirical results show that CNN outperforms DAN2 in accurately forecasting wind speed under different weather conditions. This superiority is likely due to the inherent architectural attributes of CNNs, including feature extraction capabilities, spatial hierarchy learning, and resilience to spatial variability. The results from the combined forecasts were comparable with those from the QRNN, which was slightly better than those from the GAQR model. However, the combined forecasts were more accurate than the individual models. These results could be useful to decision-makers in the energy sector. Full article
(This article belongs to the Special Issue Advance Techniques for Solar Radiation, Wind Speed and Photovoltaic Forecasting)
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21 pages, 3642 KiB  
Article
Utilizing the Honeybees Mating-Inspired Firefly Algorithm to Extract Parameters of the Wind Speed Weibull Model
by Abubaker Younis, Fatima Belabbes, Petru Adrian Cotfas and Daniel Tudor Cotfas
Forecasting 2024, 6(2), 357-377; https://doi.org/10.3390/forecast6020020 - 22 May 2024
Cited by 1 | Viewed by 1129
Abstract
This study introduces a novel adjustment to the firefly algorithm (FA) through the integration of rare instances of cannibalism among fireflies, culminating in the development of the honeybee mating-based firefly algorithm (HBMFA). The IEEE Congress on Evolutionary Computation (CEC) 2005 benchmark functions served [...] Read more.
This study introduces a novel adjustment to the firefly algorithm (FA) through the integration of rare instances of cannibalism among fireflies, culminating in the development of the honeybee mating-based firefly algorithm (HBMFA). The IEEE Congress on Evolutionary Computation (CEC) 2005 benchmark functions served as a rigorous testing ground to evaluate the efficacy of the new algorithm in diverse optimization scenarios. Moreover, thorough statistical analyses, including two-sample t-tests and fitness function evaluation analysis, the algorithm’s optimization capabilities were robustly validated. Additionally, the coefficient of determination, used as an objective function, was utilized with real-world wind speed data from the SR-25 station in Brazil to assess the algorithm’s applicability in modeling wind speed parameters. Notably, HBMFA achieved superior solution accuracy, with enhancements averaging 0.025% compared to conventional FA, despite a moderate increase in execution time of approximately 18.74%. Furthermore, this dominance persisted when the algorithm’s performance was compared with other common optimization algorithms. However, some limitations exist, including the longer execution time of HBMFA, raising concerns about its practical applicability in scenarios where computational efficiency is critical. Additionally, while the new algorithm demonstrates improvements in fitness values, establishing the statistical significance of these differences compared to FA is not consistently achieved, which warrants further investigation. Nevertheless, the added value of this work lies in advancing the state-of-the-art in optimization algorithms, particularly in enhancing solution accuracy for critical engineering applications. Full article
(This article belongs to the Special Issue Advance Techniques for Solar Radiation, Wind Speed and Photovoltaic Forecasting)
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24 pages, 4421 KiB  
Article
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; https://doi.org/10.3390/forecast5010009 - 27 Jan 2023
Cited by 3 | Viewed by 2439
Abstract
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
(This article belongs to the Special Issue Advance Techniques for Solar Radiation, Wind Speed and Photovoltaic Forecasting)
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