Wind speed forecasting using hybrid ANN-Kalman Filter techniques

Abstract

Wind intermittency, independent nature of direction, speed was the best-known challenge and major barrier against wind power penetration. Precise forecasting of wind speed is vital to the effective harvesting of wind power. The problems posed in the wind speed prediction include reduction in time delay, improvement in speed for short time, error reduction, model improvement for effective conversion of wind energy. However there is a lot of research being done in this field in which individual as well as hybrid techniques are being worked upon. The objective of this paper is on error reduction and improvement of model by hybridizing two techniques. One is the Artificial Neural Networks (ANN) along with a statistical method of Ensemble Kalman Filter (EnKF) technique. These methods are used for short term predictions of wind speed. This result is tested practically on MATLAB in this paper. By help of observations, the EnKF will correct the output of ANN to find the best estimate of wind speed. Results in MATLAB show that combination of ANN with EnKF acts as an output correction scheme.