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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
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

Cite: Tomohiro Hachino, Hitoshi Takata, Seiji Fukushima, Yasutaka Igarashi, and Keiji Naritomi, "Multistep Ahead Prediction of Electric Power Systems Using Multiple Gaussian Process Models," Jounal of Automation and Control Engineering, Vol. 3, No. 4, pp. 316-321, August, 2015. doi: 10.12720/joace.3.4.316-321
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