The Bulletin of Irkutsk State University. Series Mathematics
 RUS  ENG JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB
 General information Latest issue Archive Search papers Search references RSS Latest issue Current issues Archive issues What is RSS

 The Bulletin of Irkutsk State University. Series Mathematics: Year: Volume: Issue: Page: Find

 The Bulletin of Irkutsk State University. Series Mathematics, 2014, Volume 9, Pages 75–90 (Mi iigum201)

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

V. G. Kurbatskya, V. A. Spiryaeva, N. V. Tomina, P. Leahyb, D. N. Sidorovca, A. V. Zhukovc

a Energy Systems Institute, Siberian Branch of Russian Academy of Sciences
b University College Cork
c Institute of Mathematics, Economics and Informatics, Irkutsk State University

Abstract: A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert–Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector 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 of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

Keywords: time series, forecasting, integral transforms, ANN, SVM, machine learning, boosting, singular integral, feature analysis.

Full text: PDF file (409 kB)
References: PDF file   HTML file
UDC: 518.517
Language:

Citation: V. G. Kurbatsky, V. A. Spiryaev, N. V. Tomin, P. Leahy, D. N. Sidorov, A. V. Zhukov, “Power System Parameters Forecasting Using Hilbert–Huang Transform and Machine Learning”, The Bulletin of Irkutsk State University. Series Mathematics, 9 (2014), 75–90

Citation in format AMSBIB
\Bibitem{KurSpiTom14} \by V.~G.~Kurbatsky, V.~A.~Spiryaev, N.~V.~Tomin, P.~Leahy, D.~N.~Sidorov, A.~V.~Zhukov \paper Power System Parameters Forecasting Using Hilbert--Huang Transform and Machine Learning \jour The Bulletin of Irkutsk State University. Series Mathematics \yr 2014 \vol 9 \pages 75--90 \mathnet{http://mi.mathnet.ru/iigum201}