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Multistep ahead prediction of electric power systems using multiple gaussian process models

This paper focuses on the problem of multistep ahead prediction of electric power systems using the Gaussian process models. The Gaussian process model is a nonparametric model and the output of the model has Gaussian distribution with mean and variance | Journal of Automation and Control Engineering Vol. 3, No. 4, August 2015 Multistep Ahead Prediction of Electric Power Systems Using Multiple Gaussian Process Models Tomohiro Hachino, Hitoshi Takata, Seiji Fukushima, Yasutaka Igarashi, and Keiji Naritomi Kagoshima University, Kagoshima, Japan Email: {hachino, takata, fukushima, igarashi}@eee.kagoshima-u.ac.jp, k0519282@kadai.jp Abstract—This paper focuses on the problem of multistep ahead prediction of electric power systems using the Gaussian process models. The Gaussian process model is a nonparametric model and the output of the model has Gaussian distribution with mean and variance. The multistep ahead prediction for the phase angle in transient state of the electric power system is accomplished by using multiple Gaussian process models as every step ahead predictors in accordance with the direct approach. The proposed prediction method gives the predictive values of the phase angle and the uncertainty of the predictive values as well. Simulation results for a simplified electric power system are shown to illustrate the effectiveness of the proposed prediction method. Index Terms—multistep ahead prediction, Gaussian process model, direct method, electric power system I. INTRODUCTION In recent years, model predictive control (MPC) has received much attention in both process control and servo control [1]-[5]. The performance of MPC greatly depends on the accuracy of the model used for prediction. Therefore, to improve the performance of MPC, it is urgent to develop an accurate predictor. The Gaussian process (GP) model is one of the attractive models for multistep ahead prediction. The GP model is a nonparametric model and fits naturally into Bayesian framework [6]-[8]. This model has recently attracted much attention for system identification [9], [10], time series forecasting [11]-[13], and predictive control [3], [14], [15]. Since the GP model gives us not only the mean value but also the variance of the