Statistical bivariate modelling of wind using first-order Markov chain and Weibull distribution
By F.Youcef Ettoumi, H. Sauvageot, A.-E.-H Adane. 2003
108 citations recorded by the source corpus.
This paper studies the statistical features of the wind at Oran (Algeria). The data used are the wind speed and wind direction measurements collected every 3 h at the meteorological station of Es Senia (Oran), during the 1982/92 period. The eight directions of the compass card have been considered to build the frequency distribution of the wind speed for each month of the year and each direction. The three-hourly wind data have been modelled by means of Markov chains. First-order nine-state Markov chains are found to fit well the wind direction data, whereas the related wind speed data are well fitted by first-order three-state Markov chains. The Weibull probability distribution function has also been considered and found to fit the monthly frequency distributions of wind speed measurements. Two methods of wind data retrieval are thus made available. In fact, two models of chronological bi-series are obtained describing wind speed and wind direction.
Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/deeb53672077957dffceebc7ccc026478bd28635
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Citations in the corpus
- Smart Grid — The New and Improved Power Grid: A Survey (2012, unknown): “In addition, finite-state Markov models have also been used as an effective approach to characterize the correlation structure of the renewable energy outputs [66], [181].”
- Optimal Management and Sizing of Energy Storage Under Dynamic Pricing for the Efficient Integration of Renewable Energy (2015, background): “Researchers have shown that wind speed and energy can be modeled effectively using autoregressive (AR) processes that have time varying parameters to account for seasonal characteristics of wind throughout the year [23], [24].”