Table of Contents
ISRN Renewable Energy
Volume 2013, Article ID 657437, 9 pages
Research Article

Wind Speed Estimation: Incorporating Seasonal Data Using Markov Chain Models

1Department of Business Administration, Faculty of Economics and Administrative Sciences, Yalova University, 77100 Yalova, Turkey
2Engineering Department, Faculty of Agriculture, Dalhousie University, Truro, NS, Canada B2N 5E3

Received 3 October 2013; Accepted 27 October 2013

Academic Editors: M. Benghanem, P. D. Lund, and S. Rehman

Copyright © 2013 Selin Karatepe and Kenneth W. Corscadden. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.