Solar Irradiance and Wind Forecasting

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: 4 July 2024 | Viewed by 1319

Special Issue Editors

Department of Mechanical Engineering, Technology Center, Federal University of Ceará, Fortaleza 60020-181, Brazil
Interests: renewable energy; remote sensing; applied numerical methods for the environment; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world is constantly witnessing ground-breaking advancements in forecasting technologies, which are being integrated into daily life and sectors such as economics, medicine, and meteorology. Consequently, the significance of these developments for the well-being of modern society is undeniable. In this context, renewable energy sources, particularly solar and wind, have experienced increasing benefits from these advances, as accurate predictions of their behavior lead to both financial gains and resource conservation.

As such, we advocate for the further development and exploration of solar and wind resource forecasting techniques. The primary goal of this Special Issue is to enhance our understanding of forecasting methodologies, successful strategies, and the factors governing interactions that yield the most reliable outcomes. We aim to provide science-based knowledge, innovative ideas/approaches, and solutions in solar and wind forecasting. We invite authors to share their insights, expertise, and accomplishments concerning new modeling paradigms, variable importance, uncertainty evaluation, and the use of remote sensing data and related information. Moreover, this Special Issue also welcomes reviews on best practices in solar and wind forecasting. In particular, the following topics are of significant interest:

  • Evaluation of physical, statistical, or machine-learning-based models;
  • Developments in environmental forecasting;
  • Examining the effects of uncertainty on decision-making processes;
  • Innovative forecasting approaches;
  • The influence and interplay of forecasting on key stakeholders;
  • The impact of global warming and climate change on solar and wind forecasting.

Prof. Dr. Paulo Rocha
Prof. Dr. Bahram Gharabaghi
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 2400 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.


  • solar radiation
  • wind speed
  • meteorology
  • artificial intelligence
  • machine learning
  • deep learning
  • forecasting
  • time series

Published Papers (1 paper)

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27 pages, 3761 KiB  
Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions
Atmosphere 2023, 14(11), 1635; - 31 Oct 2023
Viewed by 848
This paper proposes to analyze the performance increase in the forecasting of solar irradiance and wind speed by implementing a dynamic ensemble architecture for intra-hour horizon ranging from 10 to 60 min for a 10 min time step data. Global horizontal irradiance (GHI) [...] Read more.
This paper proposes to analyze the performance increase in the forecasting of solar irradiance and wind speed by implementing a dynamic ensemble architecture for intra-hour horizon ranging from 10 to 60 min for a 10 min time step data. Global horizontal irradiance (GHI) and wind speed were computed using four standalone forecasting models (random forest, k-nearest neighbors, support vector regression, and elastic net) to compare their performance against two dynamic ensemble methods, windowing and arbitrating. The standalone models and the dynamic ensemble methods were evaluated using the error metrics RMSE, MAE, R2, and MAPE. This work’s findings showcased that the windowing dynamic ensemble method was the best-performing architecture when compared to the other evaluated models. For both cases of wind speed and solar irradiance forecasting, the ensemble windowing model reached the best error values in terms of RMSE for all the assessed forecasting horizons. Using this approach, the wind speed forecasting gain was 0.56% when compared with the second-best forecasting model, whereas the gain for GHI prediction was 1.96%, considering the RMSE metric. The development of an ensemble model able to provide accurate and precise estimations can be implemented in real-time forecasting applications, helping the evaluation of wind and solar farm operation. Full article
(This article belongs to the Special Issue Solar Irradiance and Wind Forecasting)
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