Convertible wind energy based on predicted wind speed at hub-height

Mohandes, M.; Rehman, Shafiqur; Abido, M. A.; Badran, Saeed M.

doi:10.1080/15567036.2012.758677

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Wind profiling methods have been developed to predict wind speed at different heights aboveground using WS10M observations, including LOGarithmic wind profile (LOG) and POWer law (POW) methods (Monin and Obukhov 1954 ; Peterson and Hennessey 1978 ), and performances and limitations of these methods have been explored in studies (Lubitz 2009 ; Optis et al 2016 ). Recently, machine learning (ML) algorithms have been tested for wind profiling methods (Mohandes et al 2016 ; Bodini and Optis 2020 ; Vassallo et al 2020 ); however, most of these studies focused on predicting WS10M; therefore, the utility of these methods for agricultural simulation and modeling is unclear. Moreover, despite its applicability to various fields, climate models and numerical weather prediction systems predict WS10M with a coarse spatial resolution, which causes a discrepancy between the information required by agricultural management and that produced by the weather service.…”

Section: Introductionmentioning

confidence: 99%

High-resolution wind speed forecast system coupling numerical weather prediction and machine learning for agricultural studies — a case study from South Korea

2022

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Forecasting wind speed near the surface with high-spatial resolution is beneficial in agricultural management. There is a discrepancy between the wind speed information required for agricultural management and that produced by weather agencies. To improve crop yield and increase farmers’ incomes, wind speed prediction systems must be developed that are customized for agricultural needs. The current study developed a high-resolution wind speed forecast system for agricultural purposes in South Korea. The system produces a wind speed forecast at 3 m aboveground with 100-m spatial resolution across South Korea. Logarithmic wind profile, power law, random forests, support vector regression, and extreme learning machine were tested as candidate methods for the downscaling wind speed data. The wind speed forecast system developed in this study provides good performance, particularly in inland areas. The machine learning–based methods give the better performance than traditional methods for downscaling wind speed data. Overall, the random forests are considered the best downscaling method in this study. Root mean square error and mean absolute error of wind speed prediction for 48 h using random forests are approximately 0.8 m/s and 0.5 m/s, respectively.

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Section: Introductionmentioning

confidence: 99%

High-resolution wind speed forecast system coupling numerical weather prediction and machine learning for agricultural studies — a case study from South Korea

2022

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“…Karasal alanlarda yüzeye yakın yerlerde esen rüzgarlar türbülanslı iken, yükseklik arttıkça bu türbülans azalır ve kendisini daha düzgün esen bir rüzgara bırakır. Ayrıca, yükseklik arttıkça arazinin yapısına ve atmosferik şartlara bağlı olarak da rüzgâr hızı üstel bir eğri şeklinde artış gösterir [2]. Rüzgâr enerjisi basınç farklılıklarının olduğu yerler, paralel vadiler, yüksek düzlükler ve az eğimli vadilerde çok olmaktadır.…”

Section: Introductionunclassified

Türki̇ye, Samsun-Havza Rüzgâr Enerji̇ Potansi̇yeli̇ Değerlendi̇rmesi̇

Mamur¹,

Karayel²

2018

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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ÖZ EVALUATION OF WIND ENERGY POTENTIAL IN HAVZA-SAMSUN, TURKEY ABSTRACTIn this study, the wind potential measurement of the defined region has been carried out by means of a wind tower installed in Havza-Samsun, Turkey. The analyzed wind data have been accumulated and registered from 2014 to 2015 dates in 10 min intervals through the tower of 80 m having three anemometers at 80 m, 78 m, and 57 m. Moreover, other air variables have been taken with the aid of temperature, humidity and pressure transmitters placed at 5 m and 6 m heights of the tower. According to the evaluations, the average wind speed at 80 m tower height has been calculated 4.9 m/s. Also, the average pressure value at 5 m tower height, the temperature value at 5 m tower height, and the humidity value at 6 m tower height have been defined 928.52 hPa, 12.61°C, and 72.14%, respectively. Depending on this data, the energy that can be produced is found in Watts/m 2 for heights of 30 m and 80 m. Finally, the dominant wind direction of the selected region has been determined as the North direction by means of two wind direction transmitters installed at 78 m and 28 m heights. According to the obtained values, it is determined that this selected area is not suitable for large wind turbine investments.

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“…These studies include the understanding wind speed trends and inherent properties over long period using modern techniques such as wavelets and power spectrum Zheng et al [8], Alam et al [9], and Siddiqi et al [10]; wind farm layout design optimization and wind turbine selection using multi-criteria algorithms Rehman et al [11] and Rehman and Khan [12]; wind speed distribution analysis using maximum entropy principal Shoaib et al [13] and artificial neural network for vertical estimation of wind speed Mohandes and Rehman [14] and Mohandes et al [15]; spatial estimation of wind speed Mohandes et al [16]; and prediction of wind speed ahead of time Mohandes and Rehman [17], Mohandes et al [18] and Mohandes et al [19]. The local research team has also worked on wind power resources assessment and wind characteristics for offshore locations in collaboration with Greek scientists Bagiorgas et al [20][21][22][23] and Rehman et al [24] and with Algerian researchers on wind power potential utilization for onshore locations Himri et al [25][26][27].…”

Section: Saudi Arabia's Wind Power Research and Development Updatementioning

confidence: 99%

Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review

et al. 2018

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Among renewable sources of energy, wind is the most widely used resource due to its commercial acceptance, low cost and ease of operation and maintenance, relatively much less time for its realization from concept till operation, creation of new jobs, and least adverse effect on the environment. The fast technological development in the wind industry and availability of multi megawatt sized horizontal axis wind turbines has further led the promotion of wind power utilization globally. It is a well-known fact that the wind speed increases with height and hence the energy output. However, one cannot go above a certain height due to structural and other issues. Hence other attempts need to be made to increase the efficiency of the wind turbines, maintaining the hub heights to acceptable and controllable limits. The efficiency of the wind turbines or the energy output can be increased by reducing the cut-in-speed and/or the rated-speed by modifying and redesigning the blades. The problem is tackled by identifying the optimization parameters such as annual energy yield, power coefficient, energy cost, blade mass, and blade design constraints such as physical, geometric, and aerodynamic. The present paper provides an overview of the commonly used models, techniques, tools and experimental approaches applied to increase the efficiency of the wind turbines. In the present review work, particular emphasis is made on approaches used to design wind turbine blades both experimental and numerical, methodologies used to study the performance of wind turbines both experimentally and analytically, active and passive techniques used to enhance the power output from wind turbines, reduction in cut-in-speed for improved wind turbine performance, and lastly the research and development work related to new and efficient materials for the wind turbines.

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Convertible wind energy based on predicted wind speed at hub-height

Cited by 8 publications

References 13 publications

High-resolution wind speed forecast system coupling numerical weather prediction and machine learning for agricultural studies — a case study from South Korea

High-resolution wind speed forecast system coupling numerical weather prediction and machine learning for agricultural studies — a case study from South Korea

Türki̇ye, Samsun-Havza Rüzgâr Enerji̇ Potansi̇yeli̇ Değerlendi̇rmesi̇

Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review

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