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

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

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 d...

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Bibliographic Details
Main Authors: V.G. Kurbatsky, P. Leahy, V.A. Spiryaev, N.V. Tomin, D.N. Sidorov, A.V. Zhukov
Format: Article
Language:English
Published: Irkutsk State University 2014-09-01
Series:Известия Иркутского государственного университета: Серия "Математика"
Subjects:
time series
forecasting
integral transforms
ANN
SVM
machine learning
boosting
singular integral
feature analysis
Online Access:http://isu.ru/journal/downloadArticle?article=_9a0762f6e2fd4db7a22a9c79dd5b23dc&lang=eng

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http://isu.ru/journal/downloadArticle?article=_9a0762f6e2fd4db7a22a9c79dd5b23dc&lang=eng

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