Peer-Reviewed Journal Details
Mandatory Fields
Kurbatsky, V.; Spiryaev, V.; Tomin, N.; Leahy, P. & Sidorov, D.
Irkutsk State University Bulletin
Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning
Optional Fields
Wind forecast, time series prediction, artificial intelligence, neural networks
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,
Irkutsk, Russia
Grant Details
Alexander Von Humboldt Foundation