NDLI: Hierarchical sparse learning for load forecasting in cyber-physical energy systems

Content Provider	IEEE Xplore Digital Library
Author	Xinyao Sun Xue Wang Jiangwei Wu Youda Liu
Copyright Year	2013
Description	Author affiliation: Dept. of Precision Instrum., Tsinghua Univ., Beijing, China (Xinyao Sun; Xue Wang; Jiangwei Wu; Youda Liu)
Abstract	Cyber-physical energy systems, which emerges as the approach for integrating physical layers and control networks, have drawn extensively attention in recent years. Electric load forecasting is believed to be an important issue in CPES for its applications in prices determination and automatic generation control. Conventional deterministic load forecast method have drawbacks to providing information about the probability distribution of the prediction results, which are important for stochastic decision in power systems. This paper explores a hierarchical probabilistic approach for short-term load forecast, which combines sparse Bayesian learning with empirical mode decomposition, in order to obtain a componential forecasting results, as well as the forecasting uncertainty. Mahalanobis distance based similar day weighting is introduced to prune the training data. The numerical testing results illustrate that the proposed approach exhibits better performance in comparison with original SBL model and weighted SBL without componential analysis.
Sponsorship	IEEE Instrum. Meas. Soc.
Starting Page	533
Ending Page	538
File Size	390786
Page Count	6
File Format	PDF
ISBN	9781467346214
ISSN	10915281
e-ISBN	9781467346238
DOI	10.1109/I2MTC.2013.6555474
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Forecasting Load modeling Vectors Indexes Kernel Bayes methods Predictive models empirical mode decomposition cyber-physical energy systems short-term load forecasting sparse Bayesian learning similar day weighting
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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