%0 Journal Article
%T Wind Speed Estimation: Incorporating Seasonal Data Using Markov Chain Models
%A Selin Karatepe
%A Kenneth W. Corscadden
%J ISRN Renewable Energy
%D 2013
%R 10.1155/2013/657437
%X This paper presents a novel approach for accurately modeling and ultimately predicting wind speed for selected sites when incomplete data sets are available. The application of a seasonal simulation for the synthetic generation of wind speed data is achieved using the Markov chain Monte Carlo technique with only one month of data from each season. This limited data model was used to produce synthesized data that sufficiently captured the seasonal variations of wind characteristics. The model was validated by comparing wind characteristics obtained from time series wind tower data from two countries with Markov chain Monte Carlo simulations, demonstrating that one month of wind speed data from each season was sufficient to generate synthetic wind speed data for the related season. 1. Introduction One of the most challenging features of wind energy application is the uncertainty of the wind resource. Wind speed and hence wind energy potential has a key influence on the profitability of a wind farm and on the management of transmission and distribution networks by utility systems operators. Accurate and reliable data related to both long-term and short-term wind characteristics are essential for site selection and technology specification [1]. Wind data acquisition is typically required for one to two years and is achieved through the use of anemometers and 60£żm meteorological towers. Because of the stochastic nature of the wind resource, long-term wind speed recordings are required to characterize wind patterns, but in some cases wind data may be corrupted or missing for short periods, making accurate and reliable prediction of wind characteristics difficult. Operational wind speed prediction is also a challenge with historic data playing a major role in the prediction of future wind data, both of which require long-term reliable and complete historic data. The topic of wind speed prediction has been addressed by a number of researchers with approaches typically based on the analysis of historical time series wind data [2] or model based using a range of meteorological and/or local topographical input data. Lei et al. [3] provide a comprehensive overview of wind speed forecasting and power prediction methods using numerical and statistical approaches and conclude that different models have different strengths based on inputs and applications such as short-term or long-term prediction. Wind speed data is typically analyzed to characterize the potential annual resource, presented, for example, as a Weibull Density Function [4] for the estimation of wind
%U http://www.hindawi.com/journals/isrn.renewable.energy/2013/657437/