Abstract
The ability to predict wind speeds is very important for the security and stability of wind farms and power system operations. Wind speeds typically vary slowly over time, which makes them difficult to forecast. In this study, a hybrid nonlinear estimation approach combining Gaussian process (GP) and unscented Kalman filter (UKF) is proposed to predict dynamic changes of wind speed and improve forecasting accuracy. The proposed approach can provide both point and interval predictions for wind speed. Firstly, the GP method is established as the nonlinear transition function of a state space model, and the covariance obtained from the GP predictive model is used as the process noise. Secondly, UKF is used to solve the state space model and update the initial prediction of short-term wind speed. The proposed hybrid approach can adjust dynamically in conjunction with the distribution changes. In order to evaluate the performance of the proposed hybrid approach, the persistence model, GP model, autoregressive (AR) model, and AR integrated with Kalman filter (KF) model are used to predict the results for comparison. Taking two wind farms in China and the National Renewable Energy Laboratory (NREL) database as the experimental data, the results show that the proposed hybrid approach is suitable for wind speed predictions, and that it can increase forecasting accuracy.
Highlights
The 13th annual report by the Global Wind Energy Council states that wind power is leading the charge in the transition away from fossil fuels, and is the most competitively priced technology in many markets [1]
Gaussian process (GP) model, and the hyperparameters estimated by was used as the covariance function of the GP model, and the hyperparameters were estimated by maximum likelihood
This study proposed a hybrid approach GP-unscented Kalman filter (UKF) for short-term wind speed prediction
Summary
The 13th annual report by the Global Wind Energy Council states that wind power is leading the charge in the transition away from fossil fuels, and is the most competitively priced technology in many markets [1]. UKF can provide equal or better results than EKF. In order to make the GP model more suitable for wind speed prediction, a hybrid nonlinear forecasting strategy is proposed, comprising a state space model based on the UKF. The. GP is used to establish the nonlinear state space equation and obtain preliminary short-term wind speed predictions. The UKF method is used to solve the nonlinear state space equation and update the preliminary prediction results. (1) to establish a GP-UKF hybrid model for short-term predictions of wind speed; (2) to follow the slow time-varying characteristic of wind speed; (3) to provide better point and confidence interval predictions simultaneously for short-term wind speed. This paper is organized as follows: Section 2 presents the proposed forecasting approach GP-UKF.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
