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Atmosphere
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Advances in Wind and Wind Power Forecasting and Diagnostics
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Advances in Wind and Wind Power Forecasting and Diagnostics

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

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 9879

Special Issue Editor

Dr. William Cheng

E-Mail Website
Guest Editor
National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307, USA
Interests: wind energy; numerical weather prediction; mesoscale modeling; machine learning; cyclogenesis; regional climate modeling

Special Issue Information

Dear Colleagues,

Wind forecasting can be carried out utilizing a number of methods. Wind forecasts can come from numerical prediction models where the dynamical and thermodynamical variables are solved from a set of coupled partial differential equations based on the principles of geophysical fluid dynamics and thermodynamics or empirical models using statistics or machine learning. Additionally, wind forecasts can be derived by combining numerical weather forecasts and statistical or machine learning models.

Wind diagnostics may employ methods to characterize the temporal and spatial variabilities using methods such as Fourier analysis. In addition, machine learning methods such as self-organizing maps have been used to identify different wind regimes.

Many countries are adopting renewable energy, such as wind energy, in order to reduce the consumption of fossil fuels to combat pollution and climate change. However, accurate prediction of wind speed/power is essential in power grid integration due to the intermittent nature of wind power. Wind speed forecasts from numerical weather prediction models have been used to provide wind power forecasts by using the power curves or diagnostic relationship between wind speed and wind power. Machine learning models have also been used with numerical weather forecasts to provide wind power forecasts.

Manuscripts on all aspects of wind and wind power forecasting and diagnostics are welcome for this Special Issue.

Dr. William Cheng
Guest Editor

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. 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.

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Published Papers (5 papers)

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Research

23 pages, 54366 KiB  
Article
Study on Wind Profile Characteristics Using Cluster Analysis
by Yanru Wang, Shengbao Tian, Bin Fu, Maoyu Zhang, Xu Wang, Shuqin Zheng, Chuanxiong Zhang and Lei Zhou
Atmosphere 2024, 15(6), 708; https://doi.org/10.3390/atmos15060708 - 13 Jun 2024
Viewed by 1151
Abstract
The accurate characterization of typhoon wind profile properties is of great importance in the field of wind engineering and wind design of high-rise structures. In this paper, the average typhoon wind profile characteristics are investigated using the 930 m height measurement data of [...] Read more.
The accurate characterization of typhoon wind profile properties is of great importance in the field of wind engineering and wind design of high-rise structures. In this paper, the average typhoon wind profile characteristics are investigated using the 930 m height measurement data of Typhoon Lekima 2019 obtained from the observations of the mobile acoustic profiling radar deployed in the coastal area. Specifically, this paper adopts a cohesive hierarchical cluster analysis method to classify the mean wind profiles of Super Typhoon Lekima 2019, and the optimal number of clusters is obtained as two classes by the profile coefficient with the sum of squares of clustering errors, the Calinski–Harabasz index, and the Davies–Bouldin index, and the two classes of typical wind profiles are named as cluster 1 type and cluster 2 type. The model fitting analysis of the two types of typical wind profiles was carried out in the height range of 0~300 m after classification, and the effects of fitting the cluster 1-type mean wind profiles with the Vickery model and the Snaiki and Wu model and the cluster 2-type mean wind profiles with the Power-law model, the Log-law model, and the Deaves–Harris and Gryning models were discussed. The results show that the cohesive hierarchical cluster analysis method used in this paper can effectively categorize the mean typhoon profiles. In addition, this paper has some reference significance for future research on the characteristics of measured typhoon wind profiles and engineering applications such as the wind-resistant design of high-rise structures. Full article
(This article belongs to the Special Issue Advances in Wind and Wind Power Forecasting and Diagnostics)
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17 pages, 4166 KiB  
Article
Exploring the Potential of Sentinel-1 Ocean Wind Field Product for Near-Surface Offshore Wind Assessment in the Norwegian Arctic
by Eduard Khachatrian, Patricia Asemann, Lihong Zhou, Yngve Birkelund, Igor Esau and Benjamin Ricaud
Atmosphere 2024, 15(2), 146; https://doi.org/10.3390/atmos15020146 - 24 Jan 2024
Cited by 2 | Viewed by 2168
Abstract
The exploitation of offshore wind resources is a crucial step towards a clean energy future. It requires an advanced approach for high-resolution wind resource evaluations. We explored the suitability of the Sentinel-1 Level-2 OCN ocean wind field (OWI) product for offshore wind resource [...] Read more.
The exploitation of offshore wind resources is a crucial step towards a clean energy future. It requires an advanced approach for high-resolution wind resource evaluations. We explored the suitability of the Sentinel-1 Level-2 OCN ocean wind field (OWI) product for offshore wind resource assessments. The SAR data were compared to in situ observations and three reanalysis products: the global reanalysis ERA5 and two regional reanalyses CARRA and NORA3. This case study matches 238 scenes from 2022 for the Goliat station, an oil platform located 85 km northwest of Hammerfest in the Barents Sea, where a new offshore wind park has been proposed. The analysis showed that despite their unique limitations in spatial and temporal resolutions, all data sources have similar statistical properties (RMSE, correlation coefficient, and standard deviation). The Weibull parameters characterizing the wind speed distributions showed strong similarities between the Sentinel-1 and all reanalysis data. The Weibull parameters of the in situ measurements showed an underestimation of wind speed compared to all other sources. Comparing the full reanalysis datasets with the subsets matching the SAR scenes, only slight changes in Weibull parameters were found, indicating that, despite its low temporal resolution, the Sentinel-1 Level 2 OWI product can compete with the more commonly used reanalysis products in the estimation of offshore wind resources. Its high spatial resolution, which is unmatched by other methods, renders it especially valuable in offshore areas close to complex coastlines and in resolving weather events at a smaller scale. Full article
(This article belongs to the Special Issue Advances in Wind and Wind Power Forecasting and Diagnostics)
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13 pages, 8917 KiB  
Article
Study of the Characteristics of the Long-Term Persistence of Hourly Wind Speed in Xinjiang Based on Detrended Fluctuation Analysis
by Xiuqin Wang, Xinyu Lu, Qinglei Li, Hongkui Zhou, Cheng Li and Xiaohui Zou
Atmosphere 2024, 15(1), 37; https://doi.org/10.3390/atmos15010037 - 28 Dec 2023
Cited by 1 | Viewed by 1202
Abstract
Profound research on the characteristics of the long-term persistence of wind is greatly significant for understanding the characteristics of wind speed mechanisms as well as for avoiding disasters caused by wind. In the current study, we selected the hourly 10 min wind speed [...] Read more.
Profound research on the characteristics of the long-term persistence of wind is greatly significant for understanding the characteristics of wind speed mechanisms as well as for avoiding disasters caused by wind. In the current study, we selected the hourly 10 min wind speed series between 2017 and 2021 from 105 nation-level meteorological stations in Xinjiang and investigated the spatiotemporal variations in the long-term persistence of wind speed in different regions of Xinjiang and in different seasons using detrended fluctuation analysis. The main findings are as follows: (1) The wind speed in Xinjiang shows noticeable annual and seasonal variations, exhibiting satisfactory long-term sustainability. Winter has the best long-term sustainability, followed sequentially by spring, autumn, and summer because of wind speed stability. (2) The long-term persistence of hourly wind speed in Xinjiang exhibits remarkable regionality, with regions with strong wind superior to the remaining regions. (3) The long-term persistence of wind speed within the same season is primarily associated with wind speed magnitude and the dispersion degree between 90% and 100% of the wind speed numerical values. A higher wind speed indicates better long-term persistence. At the same speed, the more discrete the numerical values in the 90–100% distribution range, the better the persistence. Full article
(This article belongs to the Special Issue Advances in Wind and Wind Power Forecasting and Diagnostics)
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23 pages, 7346 KiB  
Article
An Ensemble Forecast Wind Field Correction Model with Multiple Factors and Spatio-Temporal Features
by Min Chen, Hao Yang, Bo Mao, Kaiwen Xie, Chaoping Chen and Yuanchang Dong
Atmosphere 2023, 14(11), 1650; https://doi.org/10.3390/atmos14111650 - 3 Nov 2023
Cited by 4 | Viewed by 1806
Abstract
Accurate wind speed prediction is significantly important for the full utilization of wind energy resources and the improvement in the economic benefits of wind farms. Because the ensemble forecast takes into account the uncertainty of information about the atmospheric motion, domestic and foreign [...] Read more.
Accurate wind speed prediction is significantly important for the full utilization of wind energy resources and the improvement in the economic benefits of wind farms. Because the ensemble forecast takes into account the uncertainty of information about the atmospheric motion, domestic and foreign weather service forecast centers often choose to use the ensemble numerical forecast to achieve the fine forecast of wind speed. However, due to the unavoidable systematic errors of the ensemble numerical forecast model, it is necessary to correct the deviation in the ensemble numerical forecast wind speed. Considering the typical spatio-temporal characteristics of the grid prediction data of the wind field, based on Convolutional Long–Short Term Memory (ConvLSTM) units and attention mechanism, this paper takes the complex and representative North China region as the research area, aiming to reveal the shortcomings of existing deep learning integrated prediction correction models in extracting temporal features of grid prediction data. We propose a new ensemble prediction wind field correction model integrating multi-factor and spatio-temporal characteristics. This model uses reanalyzed land data provided by the European Center for Medium-Range Weather Forecasts as the real data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical prediction model of the China Meteorological Administration. We used the reanalyzed land data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the live data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical forecast model of the China Meteorological Administration (CMA). At the same time, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used as the scoring indicators, and the results of the China Meteorological Administration–Regional Ensemble Prediction System (CMA–REPS) ensemble average, multiple linear regression method correction, Long–Short Term Memory (LSTM) method correction, and U-net (UNET) method correction were compared. Compared with the UNET model method, the experimental results show that when processing the 10 m zonal wind data, 10 m meridional wind data, and 10 m average wind speed data of CMA–REPS 24 h forecasts, the correction results of our model can reduce the RMSE score index by 9.15%, 4.83%, and 7.79%. At the same time, when processing the 48 h and 72 h near-surface 10 m wind field data of the CMA–REPS forecast, our model can improve the prediction accuracy of CMA–REPS near-surface wind forecast data. Therefore, the correction effect of the proposed model in a complex terrain area is evidently better compared to other methods. Full article
(This article belongs to the Special Issue Advances in Wind and Wind Power Forecasting and Diagnostics)
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18 pages, 4707 KiB  
Article
A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey
by R. K. Reja, Ruhul Amin, Zinat Tasneem, Sarafat Hussain Abhi, Uzair Aslam Bhatti, Subrata Kumar Sarker, Qurat ul Ain and Yazeed Yasin Ghadi
Atmosphere 2023, 14(10), 1516; https://doi.org/10.3390/atmos14101516 - 30 Sep 2023
Cited by 2 | Viewed by 2199
Abstract
The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively [...] Read more.
The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively in wind power-producing facilities. This study’s short-term wind speed estimations are made using a multivariate model based on an artificial neural network (ANN) that combines several local measurements, including wind speed, wind direction, LV active power, and theoretical power curve. The dataset was received from Turkey’s SCADA system at 10-min intervals, and the actual data validated the expected performance. The research took wind speed into account as an input parameter and created a multivariate model. To perform prediction outcomes on time series data, an algorithm such as an artificial neural network (ANN) is utilized. The experiment verdicts reveal that the ANN algorithm produces reliable predicting results when metrics like 0.693 for MSE, 0.833 for RMSE and 0.96 for R-squared or Co-efficient of determination are considered. Full article
(This article belongs to the Special Issue Advances in Wind and Wind Power Forecasting and Diagnostics)
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