Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function

Shu, Z.R.; Li, Qiusheng; Chan, Pak Wai

doi:10.1016/j.apenergy.2015.07.027

Cited by 142 publications

(50 citation statements)

References 40 publications

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“…In fact, Weibull has been widely applied as a tool to assess the potential of wind energy by many researchers, including [12]. Malaysian researchers alike too have utilized wind data from MMD stations (10 m height) and extrapolated them to higher height using the standard 1/7 law as the value of power law exponent.…”

Section: Introductionmentioning

confidence: 99%

Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia

Albani

Ibrahim

2017

Energies

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This paper investigated the wind energy potential by analysing a certain amount of gathered 10-min measured data at four stations located at coastal sites in Malaysia, i.e., Kudat, Mersing, Kijal, and Langkawi. The wind data are collected from a total of four new wind measurement masts with sensors mounted at various heights on the tower. The measured data have enabled the establishment of wind resource maps and the power law indexes (PLIs) analysis. In addition, the dependence of PLI upon surface temperature and terrain types is studied, as they are associated to the form of exponential fits. Moreover, the accuracy of exponential fits is assessed by comparing the results with the 1/7 law via the capacity factor (CF) discrepancies. In order to do so, the wind turbine with a hub-height similar to the maximum height of the measured data at each site is selected to simulate energy production. Accordingly, the discrepancy of CF based on the extrapolated data by employing 1/7 laws and exponential fits, in spite of being computed using measured data, is determined as well. Furthermore, the large discrepancy of the wind data and the CF, which has been determined with the application of 1/7, is compared to the exponential fits. This is because; discrepancy in estimation of vertical wind speed could lead to inaccurate CF computation. Meanwhile, from the energy potential analysis based on the computed CF, only Kudat and Mersing display a promising potential to develop a medium capacity of wind turbine power, while the other sites may be suitable for wind turbines at a small scale.

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Section: Introductionmentioning

confidence: 99%

Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia

Albani

Ibrahim

2017

Energies

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“…The mathematical expressions in equations 1-10 can also be found in previous available data [14][15][16][17][18][19][20]27,28].…”

Section: Evaluation Of Wind Power Density (Wpd)mentioning

confidence: 99%

“…Furthermore, [15] stated that "wind energy assessment is a topic of both scientific interest and an issue of relevance with ecological, economic and political implications". While wind energy has been continually exploited by nations with large wind resources [15][16][17][18][19][20], there exists limited evidence of such studies in Nigeria where electricity supply is still below demand [1]. Residential power supply in small communities and rural environments have been severely impacted by this crisis thereby posing a challenge to comfort, health and productivity of residents, culminating in high poverty levels and low Gross Domestic Product (GDP).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Wind Energy Potential for Small Communities in South-South Nigeria: Case Study of Koluama, Bayelsa State

Akinsanola

Ogunjobi

Abolude³

et al. 2017

J Fundam Renewable Energy Appl

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“…Using Equation (1), the average wind speed and the standard deviation of the wind speed data can be calculated in the equations [7,[33][34][35]:…”

Section: Moment Methodsmentioning

confidence: 99%

“…Thus, it is important to determine how closely the data from Weibull distribution estimates match data from actual wind speed distribution. Weibull distribution is a two-parameter function and these parameters-named the shape parameter, k, and the scale parameter, c-determine the form and characteristics of distribution [6][7][8][9]. In addition, two-component Weibull mixtures have been developed to improve the performance under the conditions of bimodal wind speed distribution [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Kang

Huh

2018

Energies

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On Jeju Island, South Korea, an investigation was conducted to determine the best method for estimating Weibull parameters. Six methods commonly used in many fields of the wind energy industry were reviewed: the empirical, moment, graphical, energy pattern factor, maximum likelihood, and modified maximum likelihood methods. In order to improve the reliability of a research result, five-year actual wind speed data taken from nine sites with various topographical conditions were used for the estimation. Furthermore, the effect of various topographical conditions on the accuracy of the methods was analyzed and 10 bin interval types were applied to determine the most appropriate bin interval based on their performances. Weibull distributions that were estimated using these methods were compared with the observed wind speed distribution. Then the accuracy of each method was evaluated using four accuracy tests. The results showed that of the six methods, the moment method had the best performance regardless of topographical conditions, while the graphical method performed the worst. Additionally, topographical conditions did not affect the accuracy ranking of the methods for estimating the Weibull parameters, while an increase of terrain complexity resulted in an increase of discrepancy between the estimated Weibull distribution and the frequency of the observed wind speed data. In addition, the choice in bin interval greatly affected the accuracy of the graphical method while it did not depend on the accuracy of the modified maximum likelihood method.

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Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function

Cited by 142 publications

References 40 publications

Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia

Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia

Assessment of Wind Energy Potential for Small Communities in South-South Nigeria: Case Study of Koluama, Bayelsa State

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

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