Stochastic models for wind speed forecasting

Bivona, S.; Bonanno, G.; Burlon, R.; Gurrera, Davide; Leone, Cláudio

doi:10.1016/j.enconman.2010.09.010

Cited by 58 publications

(29 citation statements)

References 18 publications

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“…Zhou et al [66] comprehensively evaluated wind speed distribution models for a case study of North Dakota country sites. Wind energy estimation and analysis of wind regions through the Weibull distribution methods is widely used nowadays [35,37,[67][68][69][70][71][72][73][74][75][76][77][78][79][80].…”

Section: Introductionmentioning

confidence: 99%

Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications

2014

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Abstract:The best Weibull distribution methods for the assessment of wind energy potential at different altitudes in desired locations are statistically diagnosed in this study. Seven different methods, namely graphical method (GM), method of moments (MOM), standard deviation method (STDM), maximum likelihood method (MLM), power density method (PDM), modified maximum likelihood method (MMLM) and equivalent energy method (EEM) were used to estimate the Weibull parameters and six statistical tools, namely relative percentage of error, root mean square error (RMSE), mean percentage of error, mean absolute percentage of error, chi-square error and analysis of variance were used to precisely rank the methods. The statistical fittings of the measured and calculated wind speed data are assessed for justifying the performance of the methods. The capacity factor and total energy generated by a small model wind turbine is calculated by numerical integration using Trapezoidal sums and Simpson's rules. The results show that MOM and MLM are the most efficient methods for determining the value of k and c to fit Weibull distribution curves.

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Section: Introductionmentioning

confidence: 99%

Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications

2014

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“…Structure of a weather prediction system. [26]. Besides, further means like Kalman filter [27] or wavelet transform [28] are added to these models to enhance their precision.…”

Section: Weather Prediction Methodsmentioning

confidence: 99%

Local weather prediction system for a heating plant using cognitive approaches

Vaščák

Jaksa

Koščák

et al. 2015

Computers in Industry

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“…Many scholars have proposed new models that can allow the prediction of wind speed minutes, hours or days ahead. Many of these models are based on neural networks [16], autoregressive models [17,18], Markov chains [19][20][21][22][23][24], hybrid models where the previous mentioned models are combined [25][26][27][28][29] and other models [30][31][32][33][34]. All of these models try to catch, from the past, information on the future.…”

Section: Introductionmentioning

confidence: 99%