ARTIFICIAL HYBRID MODEL FOR FORECASTING WIND ENERGY BASED ON ENSEMBLE KALMAN FILTER

Bopit  Chainok; Wachirapond  Permpoonsinsup; Satean Thunyasrirut; Santi  Wangnipparnto

Authors

Bopit Chainok Faculty of Engineering, Pathumwan Institute of Technology, Bangkok, 10330, Thailand.
Wachirapond Permpoonsinsup Faculty of Science and Technology, Pathumwan Institute of Technology, Bangkok, 10330, Thailand.
Satean Thunyasrirut Faculty of Engineering, Pathumwan Institute of Technology, Bangkok, 10330, Thailand.
Santi Wangnipparnto Faculty of Engineering, Pathumwan Institute of Technology, Bangkok, 10330, Thailand.

Keywords:

Wind Energy, Ensemble Kalman Filter, Prediction Wind Energy Model

Abstract

This paper presents a prediction model for wind energy based on Ensemble Kalman Filter (EnKF) Model for Prediction Wind Energy on Urban Building. Ensemble Kalman Filter is a powerful tool and is a recursive filter used to solve many variable problems. The EnKF technique is used to compare to Artificial Neural Network (ANN) technique. The considerate data are on short-term observations during January to February 2018 which is gathered up from the database. There are four meteorological parameters, namely, temperature, humidity, wind speed and wind direction. The predictive results of the prediction model and conventional ANN are compared. Finally, the experimental results show that the EnKF is a capability more than ANN.

References

Chainok, B., Permpoonsinsup, W., Thunyasrirut, S., and Wangnipparnto, S. (2017). A prediction model for wind energy based on artificial neural network with extend kalman filter. J. Mat. Sci. Appl. Ener., 6(2):146-150.

Chainok, B., Tunyasrirut, S., Wangnipparnto, S., and Permpoonsinsup, W. (2017). Artificial neural network model for wind energy on urban building in bangkok. 5th International Electrical Engineering Congress. 8-10 March 2017. Pattaya, Thailand, p. 33-36.

Wagner, H.J. and Mathu, J. (2009). Introduction to Wind Energy Systems. 1. New York: Springer.