Power System Parameters Forecasting Using Hilbert–Huang Transform and Machine Learningстатья

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[1] Power system parameters forecasting using hilbert–huang transform and machine learning / V. G. Kurbatsky, V. A. Spiryaev, N. V. Tomin et al. // Известия Иркутского государственного университета. Серия Математика. — 2014. — Vol. 9. — P. 75–90. A novel hybrid data-driven approach is developed for forecasting powersystem parameters with the goal of increasing the efficiency of short-term forecastingstudies for non-stationary time-series. The proposed approach is based on mode decompo-sition and a feature analysis of initial retrospective datausing the Hilbert-Huang trans-form and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank theimportance of variables employed in the forecasting models. The Mean Decrease Giniindex is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and supportvector regression.Apart from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting ofpower system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental resultsin the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

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