Generation of Large Directional Wind Speed Data Sets for Estimation of Wind Effects with Long Return Periods

This paper presents a novel approach for accurately modeling and ultimately predicting wind speed for selected sites when incomplete data sets are available. The application of a seasonal simulation for the synthetic generation of wind speed data is achieved using the Markov chain Monte Carlo technique with only one month of data from each season. This limited data model was used to produce synthesized data that sufficiently captured the seasonal variations of wind characteristics. The model was validated by comparing wind characteristics obtained from time series wind tower data from two countries with Markov chain Monte Carlo simulations, demonstrating that one month of wind speed data from each season was sufficient to generate synthetic wind speed data for the related season.

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Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data

MethodsX ◽

10.1016/j.mex.2021.101499 ◽

2021 ◽

pp. 101499

Author(s):

Benjamin Wacker ◽

Jan Chr. Schlüter

Keyword(s):

Wind Speed ◽

Wind Power ◽

Wind Turbines ◽

Power Output ◽

Data Sets ◽

Power Curve ◽

Wind Speed Data ◽

Large Wind

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Wind power plant forecasting and power prediction methods using Machine Learning Algorithms

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/321022021 ◽

2021 ◽

Vol 10 (2) ◽

pp. 687-691

Keyword(s):

Machine Learning ◽

Wind Speed ◽

Wind Energy ◽

Wind Power ◽

Renewable Energy Sources ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Data Sets ◽

Power Prediction ◽

Wind Speed Data

A significant and eligible source such as wind energy has the potential for producing energy in a continuous and sustainable manner among renewable energy sources. However, wind energy has several challenges, such as initial investment costs, the stationary property of wind plants, and the difficulty in finding wind-efficient energy areas. In this study, wind power forecasting was performed based on daily wind speed data using machine learning algorithms. The proposed method is based on machine learning algorithms to forecast wind power values efficiently. Tests were conducted on data sets to reveal performances of machine learning algorithms. The results showed that machine learning algorithms could be used for forecasting long-term wind power values with respect to historical wind speed data. Furthermore, several machine learning models were built for analysis on the accuracy level of the respective models, i.e, the accuracy levels of the machine.

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A statistical exploration of interval-deficient wind speed data for application to wind power assessments

Journal of Energy in Southern Africa ◽

10.17159/2413-3051/2019/v30i4a5694 ◽

2019 ◽

Vol 30 (4) ◽

pp. 13-25

Author(s):

F. Lubbe ◽

T. Harms ◽

J. Maritz

Keyword(s):

Wind Speed ◽

Wind Power ◽

Cost Reduction ◽

Data Sets ◽

Data Set ◽

Shape Factors ◽

Weibull Parameters ◽

Wind Speed Data ◽

Relative Errors

Gathering quality wind speed data can be time-consuming and expensive. The present study established whether interval-deficient wind speed data could be rendered useful for wind power assessments. The effect of interval deficiency on the quality of the wind speed data was investigated by studying the behaviour of the Weibull scale and shape factors as the interval size between wind speed measurements increased. Five wind speed data sets obtained from the Southern African Universities Radiometric Network (Sauran) were analysed, based on a proposed procedure to find the true Weibull parameters from an interval-deficient wind speed data set. It was found that the relative errors in the Weibull parameters were, on average, less than 1%, compared with the Weibull parameters computed from a wind speed data set that complies with the IEC 61400-12-1:2005(E) standard. This finding may contribute to time and cost reduction in wind power assessments. It may also promote the application of statistical methods in the renewable energy sector.

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