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Atmosphere
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Solar Irradiance and Wind Forecasting
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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: closed (4 July 2024) | Viewed by 5000

Special Issue Editors

Prof. Dr. Paulo Rocha

E-Mail Website
Guest Editor
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
Prof. Dr. Bahram Gharabaghi

E-Mail Website
Guest Editor
School of Engineering, University of Guelph, Guelph, ON NIG 2W1, Canada
Interests: hydrology; sediment transport; water quality; watershed management; climate change; water resources
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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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. Atmosphere is an international peer-reviewed open access monthly 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 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.

Keywords

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

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Published Papers (5 papers)

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Research

17 pages, 3949 KiB  
Article
Assessment of Numerical Forecasts for Hub-Height Wind Resource Parameters during an Episode of Significant Wind Speed Fluctuations
by Jingyue Mo, Yanbo Shen, Bin Yuan, Muyuan Li, Chenchen Ding, Beixi Jia, Dong Ye and Dan Wang
Atmosphere 2024, 15(9), 1112; https://doi.org/10.3390/atmos15091112 - 13 Sep 2024
Viewed by 492
Abstract
This study conducts a comprehensive evaluation of four scenario experiments using the CMA_WSP, WRF, and WRF_FITCH models to enhance forecasts of hub-height wind speeds at multiple wind farms in Northern China, particularly under significant wind speed fluctuations during high wind conditions. The experiments [...] Read more.
This study conducts a comprehensive evaluation of four scenario experiments using the CMA_WSP, WRF, and WRF_FITCH models to enhance forecasts of hub-height wind speeds at multiple wind farms in Northern China, particularly under significant wind speed fluctuations during high wind conditions. The experiments apply various wind speed calculation methods, including the Monin–Obukhov similarity theory (ST) and wind farm parameterization (WFP), within a 9 km resolution framework. Data from four geographically distinct stations were analyzed to assess their forecast accuracy over a 72 h period, focusing on the transitional wind events characterized by substantial fluctuations. The CMA_WSP model with the ST method (CMOST) achieved the highest scores across the evaluation metrics. Meanwhile, the WRF_FITCH model with the WFP method (FETA) demonstrated superior performance to the other WRF models, achieving the lowest RMSE and a greater stability. Nevertheless, all models encountered difficulties in predicting the exact timing of extreme wind events. This study also explores the effects of these methods on the wind power density (WPD) distribution, emphasizing the boundary layer’s influence at the hub-heighthub-height of 85 m. This influence leads to significant variations in the central and coastal regions. In contrast to other methods that account for the comprehensive effects of the entire boundary layer, the ST method primarily relies on the near-surface 10 m wind speed to calculate the hub-height wind speed. These findings provide important insights for enhancing wind speed and WPD forecasts under transitional weather conditions. Full article
(This article belongs to the Special Issue Solar Irradiance and Wind Forecasting)
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19 pages, 4999 KiB  
Article
Study on Downscaling Correction of Near-Surface Wind Speed Grid Forecasts in Complex Terrain
by Xin Liu, Zhimin Li and Yanbo Shen
Atmosphere 2024, 15(9), 1090; https://doi.org/10.3390/atmos15091090 - 8 Sep 2024
Viewed by 638
Abstract
Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and [...] Read more.
Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and October 2022, employing the random forest algorithm to establish and evaluate a downscaling correction model for near-surface 1 km resolution wind-speed grid forecast in the complex terrain area of northwestern Hebei Province. The results indicate that after downscaling correction, the spatial distribution of grid forecast wind speeds in the entire complex terrain study area becomes more refined, with spatial resolution improving from 3 km to 1 km, reflecting fine-scale terrain effects. The accuracy of the corrected wind speed forecast significantly improves compared to the original model, with forecast errors showing stability in both time and space. The mean bias decreases from 2.25 m/s to 0.02 m/s, and the root mean square error (RMSE) decreases from 3.26 m/s to 0.52 m/s. Forecast errors caused by complex terrain, forecast lead time, and seasonal factors are significantly reduced. In terms of wind speed categories, the correction significantly improves forecasts for wind speeds below 8 m/s, with RMSE decreasing from 2.02 m/s to 0.59 m/s. For wind speeds above 8 m/s, there is also a good correction effect, with RMSE decreasing from 2.20 m/s to 1.65 m/s. Selecting the analysis of the Zhangjiakou strong wind process on 26 April 2022, it was found that the downscaled corrected forecast wind speed is very close to the observed wind speed at the station and the ground truth grid points. The correction effect is particularly significant in areas affected by strong winds, such as the Bashang Plateau and valleys, which has significant reference value. Full article
(This article belongs to the Special Issue Solar Irradiance and Wind Forecasting)
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22 pages, 5834 KiB  
Article
A Novel Hybrid Method for Multi-Step Short-Term 70 m Wind Speed Prediction Based on Modal Reconstruction and STL-VMD-BiLSTM
by Xuanfang Da, Dong Ye, Yanbo Shen, Peng Cheng, Jinfeng Yao and Dan Wang
Atmosphere 2024, 15(8), 1014; https://doi.org/10.3390/atmos15081014 - 21 Aug 2024
Cited by 1 | Viewed by 951
Abstract
In the context of achieving the goals of carbon peaking and carbon neutrality, the development of clean resources has become an essential strategic support for the low-carbon energy transition. This paper presents a method for the modal decomposition and reconstruction of time series [...] Read more.
In the context of achieving the goals of carbon peaking and carbon neutrality, the development of clean resources has become an essential strategic support for the low-carbon energy transition. This paper presents a method for the modal decomposition and reconstruction of time series to enhance the prediction accuracy and performance regarding the 70 m wind speed. The experimental results indicate that the STL-VMD-BiLSTM hybrid algorithm proposed in this paper outperforms the STL-BiLSTM and VMD-BiLSTM models in forecasting accuracy, particularly in extracting nonlinearity characteristics and effectively capturing wind speed extremes. Compared with other machine learning algorithms, including the STL-VMD-LGBM, STL-VMD-SVR and STL-VMD-RF models, the STL-VMD-BiLSTM model demonstrates superior performance. The average evaluation criteria, including the RMSE, MAE and R2, for the proposed model, from t + 15 to t + 120 show improvements to 0.582–0.753 m/s, 0.437–0.573 m/s and 0.915–0.951, respectively. Full article
(This article belongs to the Special Issue Solar Irradiance and Wind Forecasting)
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20 pages, 5800 KiB  
Article
Evaluation of Scikit-Learn Machine Learning Algorithms for Improving CMA-WSP v2.0 Solar Radiation Prediction
by Dan Wang, Yanbo Shen, Dong Ye, Yanchao Yang, Xuanfang Da and Jingyue Mo
Atmosphere 2024, 15(8), 994; https://doi.org/10.3390/atmos15080994 - 19 Aug 2024
Viewed by 722
Abstract
This article aims to evaluate the performance of solar radiation forecasts produced by CMA-WSP v2.0 (version 2 of the China Meteorological Administration Wind and Solar Energy Prediction System) and to explore the application of machine learning algorithms from the scikit-learn Python library to [...] Read more.
This article aims to evaluate the performance of solar radiation forecasts produced by CMA-WSP v2.0 (version 2 of the China Meteorological Administration Wind and Solar Energy Prediction System) and to explore the application of machine learning algorithms from the scikit-learn Python library to improve the solar radiation prediction made by the CMA-WSP v2.0. It is found that the performance of the solar radiation forecasting from the CMA-WSP v2.0 is closely related to the weather conditions, with notable diurnal fluctuations. The mean absolute percentage error (MAPE) produced by the CMA-WSP v2.0 is approximately 74% between 11:00 and 13:00. However, the MAPE ranges from 193% to 242% at 07:00–08:00 and 17:00–18:00, which is greater than that observed at other daytime periods. The MAPE is relatively low (high) for both sunny and cloudy (overcast and rainy) conditions, with a high probability of an absolute percentage error below 25% (above 100%). The forecasts tend to underestimate (overestimate) the observed solar radiation in sunny and cloudy (overcast and rainy) conditions. By applying machine learning models (such as linear regression, decision trees, K-nearest neighbors, random forests regression, adaptive boosting, and gradient boosting regression) to revise the solar radiation forecasts, the MAPE produced by the CMA-WSP v2.0 is significantly reduced. The reduction in the MAPE is closely connected to the weather conditions. The models of K-nearest neighbors, random forests regression, and decision trees can reduce the MAPE in all weather conditions. The K-nearest neighbor model exhibits the most optimal performance among these models, particularly in rainy conditions. The random forest regression model demonstrates the second-best performance compared to that of the K-nearest neighbor model. The gradient boosting regression model has been observed to reduce the MAPE of the CMA-WSP v2.0 in all weather conditions except rainy. In contrast, the adaptive boosting (linear regression) model exhibited a diminished capacity to improve the CMA-WSP v2.0 solar radiation prediction, with a slight reduction in MAPE observed only in sunny (sunny and cloudy) conditions. In addition, the input feature selection has a considerable influence on the performance of the machine learning model. The incorporation of the time series data associated with the diurnal variation of solar radiation as an input feature can further improve the model’s performance. Full article
(This article belongs to the Special Issue Solar Irradiance and Wind Forecasting)
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27 pages, 3761 KiB  
Article
Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions
by Francisco Diego Vidal Bezerra, Felipe Pinto Marinho, Paulo Alexandre Costa Rocha, Victor Oliveira Santos, Jesse Van Griensven Thé and Bahram Gharabaghi
Atmosphere 2023, 14(11), 1635; https://doi.org/10.3390/atmos14111635 - 31 Oct 2023
Cited by 3 | Viewed by 1348
Abstract
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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