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Spatiotemporal analysis of wind speed via the Bayesian maximum entropy approach

  • Özlem BaydaroğluEmail author
  • Kasım Koçak
Original Article
  • 66 Downloads

Abstract

Spatiotemporal mapping of wind speed data is of great importance on determining the wind energy potential, construction zones, projecting future values and planning resources. From this point of view, monthly and yearly mean wind speed maps of Turkey are generated using the Bayesian maximum entropy (BME) approach. The BME, a nonlinear geostatistical approach, is the only method which uses not only data on the process but also all information about the data in a spatiotemporal mapping. During processing data, physical laws, hypotheses, experiences, scientific theories, high order space/time moments, various types of uncertain information, outputs of models, etc. are incorporated to the process. Hence, it maximizes knowledge about the variable processed because it uses exact and auxiliary data which belong to the regarding variable. In this study, Turkey daily mean wind speed data (m/s) measured at 10 m between the years 2010 and 2015 have been used. 0.05°, 0.1°, 0.3°, 0.5° and 1.0° have been chosen for spatial ranges and 1 day, week, month have been used for temporal ranges to determine the most appropriate intervals in the yearly forecast after lots of trials. Different kinds of local means are used so as to find proper kriging method. In the forecast process, hard data (raw data), hard data with soft data (auxiliary data), detrended hard with soft data are implemented, separately. All studies show that using hard with soft data gives the best forecast results and the most appropriate kriging method is determined as the ordinary kriging. Similarly to the other techniques which involve kriging methods, variances of errors are taken as a performance criterion. When the soft data are used, variances of the forecast errors reduce by half. In addition, all forecast results stay the interval of confidence levels and error variances of forecast results are quite low. Moreover, the BME forecast is employed for each month. Differently from the yearly forecast, the application of the detrended hard and soft data is found the most convenient experiment because it decreases the error values. From this point forth, it can be said that the usage of the soft data increases the accuracy of the forecasts. In addition, unknown wind speed values due to the lack of the stations are calculated and even wind speed values over the seas are identified. The study is the first and only spatiotemporal wind speed study used the BME approach for Turkey. It is thought that the mean wind speed map for Turkey can be considered as a current wind atlas of Turkey.

Keywords

Bayesian maximum entropy (BME) Spatiotemporal analysis Forecast Wind speed Wind atlas 

Notes

Acknowledgements

The study is a part of Ph.D. thesis which has won the award of the best Ph.D. thesis in Istanbul Technical University in 2016.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil Engineering, School of Engineering and Natural SciencesAltınbaş UniversityIstanbulTurkey
  2. 2.Department of Meteorological Engineering, Faculty of Aeronautics and Astronauticsİstanbul Technical UniversityIstanbulTurkey

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