Evaluation of a wind farm project for a smart city in the South-East Coastal Zone of Mauritius

Dhunny, A.Z.; Lollchund,; Rughooputh, S.D.D.V.

doi:10.17159/2413-3051/2016/v27i1a1565

Cited by 7 publications

(1 citation statement)

References 40 publications

(25 reference statements)

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“…There are many ways to extend this work to overcome its limitations. First, one can relax the Rayleigh criteria and predict the scale and shape parameter of the distributions, since there are sites where k 2 fits better the characterization of the wind resource [66]. Moreover, even though there are several sites with wind resources well-characterized by Weibull distributions [67][68][69][70][71][72][73][74][75][76], there are sites with wind resources not well-described by Weibull distributions.…”

Section: Discussionmentioning

confidence: 99%

Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming

et al. 2020

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Given the imminent threats of climate change, it is urgent to boost the use of clean energy, being wind energy a potential candidate. Nowadays, deployment of wind turbines has become extremely important and long-term estimation of the produced power entails a challenge to achieve good prediction accuracy for site assessment, economic feasibility analysis, farm dispatch, and system operation. We present a method for long-term wind power forecasting using wind turbine properties, statistics, and genetic programming. First, due to the high degree of intermittency of wind speed, we characterize it with Weibull probability distributions and consider wind speed data of time intervals corresponding to prediction horizons of 30, 25, 20, 15 and 10 days ahead. Second, we perform the prediction of a wind speed distribution with genetic programming using the parameters of the Weibull distribution and other relevant meteorological variables. Third, the estimation of wind power is obtained by integrating the forecasted wind velocity distribution into the wind turbine power curve. To demonstrate the feasibility of the proposed method, we present a case study for a location in Mexico with low wind speeds. Estimation results are promising when compared against real data, as shown by MAE and MAPE forecasting metrics.

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