A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site

Carta, José A.; Velázquez, Sergio

doi:10.1016/j.energy.2011.02.008

Cited by 65 publications

(26 citation statements)

References 25 publications

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“…These include the application of Artificial Neural Networks (ANNs), Adaptive neuro-fuzzy, Mixture probability distribution functions, Autoregressive Integrated Moving Average (ARIMA) the Bayesian model averaging, the ARIMA-ANN and the ARIMA-Kalman hybrid methods to model wind speed distributions [47,[51][52][53][54][55][56][57]. Despite the significance of these methods in predicting wind speeds profiles of a place, they also fall short in their inability to determine the two very important site specific wind speeds.…”

Section: Discussionmentioning

confidence: 99%

Wind Energy Study and Energy Cost of Wind Electricity Generation in Nigeria: Past and Recent Results and a Case Study for South West Nigeria

et al. 2014

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The study assessed the wind energy potential of ten selected sites in the south western region of Nigeria and carried out a cost benefit analysis of wind power generation at those sites. Twenty four years ' (1987 to 2010) wind speed data at 10 m height obtained from the Nigerian meteorological agency were employed to classify the sites wind profiles for electricity generation. The energy cost analysis of generating wind electricity from the sites was also carried out. The outcome showed that sites in Lagos and Oyo States were adequately suited for large scale generation with average wind speeds ranged between 2.9 and 5.8 m/s. Those from other sites may be suitable for small scale generation or as wind farms, with several small turbines connected together, to generate large enough wind power. The turbine matching results shows that turbines cut-in and rated wind speeds of between 2.0 and 3.0 m/s, and between 10 and 12.0 m/s respectively will be very suited to OPEN ACCESSEnergies 2014, 7 8509 all the sites, particularly those in locations outside Lagos and Oyo States. The energy cost analysis shows that generation cost can be as low as 0.02 €/kWh and as high as 5.03/kWh, depending on the turbine model employed.

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Section: Discussionmentioning

confidence: 99%

Wind Energy Study and Energy Cost of Wind Electricity Generation in Nigeria: Past and Recent Results and a Case Study for South West Nigeria

et al. 2014

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“…These are deemed most appropriate for estimating wind speeds and also wind energy production (see e.g. [24] or [25]). We thus use a Weibull distribution, directly on the wind energy production.…”

Section: Wind Power Production Modelmentioning

confidence: 99%

Support Mechanisms for Renewables: How Risk Exposure Influences Investment Incentives

Kitzing

2014

SSRN Journal

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“…Given training data with known reference and target site wind speeds, the patterns can be learnt and applied to unseen data to make predictions at the target site. MCP approaches based on the joint probability distribution function (pdf) between reference and target site wind speeds have also been proposed [25,26], although such approaches have received relatively little attention considering their attractive theoretical properties. Despite the variety of proposed approaches, MCP implementation in commercial software packages [27][28][29] is often restricted to top-down linear regression or scaling approaches, presumably due to their simplicity and empirical success.…”

Section: Introductionmentioning

confidence: 99%

Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP

Weekes

Tomlin

2014

Renewable Energy

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Highlights Measure-correlate-predict approach based on bivariate Weibull probability tested at 22 sites  Deviations from ideal bivariate Weibull behaviour investigated using observed and artificial data  Error metrics calculated using 120 test periods over an 11 year data record  Performance compared to existing regression methods using variable onsite measurement periods Keywords: measure-correlate-predict, wind resource assessment, bivariate Weibull distribution Abstract A detailed investigation of a measure-correlate-predict (MCP) approach based on the bivariate Weibull (BW) probability distribution of wind speeds at pairs of correlated sites has been conducted. Since wind speeds are typically assumed to follow Weibull distributions, this approach has a stronger theoretical basis than widely used regression MCP techniques. Building on previous work that applied the technique to artificially generated wind data, we have used long-term (11 year) wind observations at 22 pairs of correlated UK sites. Additionally, 22 artificial wind data sets were generated from ideal BW distributions modelled on the observed data at the 22 site pairs. Comparison of the fitting efficiency revealed that significantly longer data periods were required to accurately extract the BW distribution parameters from the observed data, compared to artificial wind data, due to seasonal variations. The overall performance of the BW approach was compared to standard regression MCP techniques for the prediction of the 10 year wind resource using both observed and artificially generated wind data at the 22 site pairs for multiple short-term measurement periods of 1-12 months. Prediction errors were quantified by comparing the predicted and observed values of mean wind speed, mean wind power density, Weibull shape factor and standard deviation of wind speeds at each site. Using the artificial wind data, the BW approach outperformed the regression approaches for all measurement periods.When applied to the real wind speed observations however, the performance of the BW approach was comparable to the regression approaches when using a full 12 month measurement period and generally worse than the regression approaches for shorter data periods. This suggests that real wind observations at correlated sites may differ from ideal BW distributions and hence regression approaches, which require less fitting parameters, may be more appropriate, particularly when using short measurement periods.

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A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site

Cited by 65 publications

References 25 publications

Wind Energy Study and Energy Cost of Wind Electricity Generation in Nigeria: Past and Recent Results and a Case Study for South West Nigeria

Wind Energy Study and Energy Cost of Wind Electricity Generation in Nigeria: Past and Recent Results and a Case Study for South West Nigeria

Support Mechanisms for Renewables: How Risk Exposure Influences Investment Incentives

Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP

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