The Effect of Missing Wind Speed Data on Wind Power Estimation
In this paper, the effect of possible missing data on wind power estimation is examined. One−month wind speed data obtained from wind and solar observation station which is constructed at Iki Eylul Campus of Anadolu University is used. A closed correlation is found between consecutive wind speed data that are collected for a period of 15 second. A very short time wind speed forecasting model is built by using two−input and one−output Adaptive Neuro Fuzzy Inference System (ANFIS). First, some randomly selected data from whole data are discarded. Second, 10%, 20% and 30% of all data which are randomly selected from a predefined interval (3−6 m/sec) are discarded and discarded data are forecasted. Finally, the data are fitted to Weibull distribution, Weibull distribution parameters are obtained and wind powers are estimated for all cases. The results show that the missing data has a significant effect on wind power estimation and must be taken into account in wind studies. Furthermore, it is concluded that ANFIS is a convenient tool for this kind of prediction.
KeywordsWind Speed Wind Turbine Weibull Distribution Wind Power Wind Speed Data
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- 2.Bossanyi, E.A.: Short-term wind prediction using Kalman filters. Wind Engineering 9, 1–8 (1985)Google Scholar
- 4.Beyer, H.G, Denger, T., Hausmann, J., Hoffmann, M., Rujan, P.: Short term prediction of wind speed and power output of a wind turbine with neural networks. In: EWEC 1994. 5th European Wind Energy Association Conf, pp. 349–352 (1994)Google Scholar
- 11.Jang, J.S., Sun, C.: Predicting Chaotic Time Series With Fuzzy If−Then Rules. In: Proc. IEEE Fuzzy Systems Conf., pp. 1079–1084 (1993)Google Scholar