NDLI: Hybrid model for dynamic power management

Content Provider	IEEE Xplore Digital Library
Author	Wai-Kong Lee Sze-Wei Lee Wee-Ong Siew
Copyright Year	1975
Abstract	Dynamic Power Management (DPM) is a system level power management technique that selectively shut down idle electrical components to save power. Previous works are mainly focused on certain types of prediction technique which assume that the idle period is long range dependent (heuristic prediction based on past history), random process (Markov Process) or short range dependent (Autoregressive) characteristic. However, the user behavior is highly variable and single assumption might not hold for all conditions. Thus, techniques based on the above assumptions will only be effective in certain condition only. Hence, we propose here a Hybrid Model DPM system that combines Moving Average (MA), Time Delay Neural Network (TDNN) and random walk model to perform idle period prediction. The Hybrid Model will first analyze the Long Range Dependency and central tendency of the past idle period time series, and choose the most appropriate strategy for future idle period prediction. Simulation results show that the Hybrid Model achieves higher power saving in most of the scenarios compared to the other methods.
Sponsorship	IEEE Consumer Electronics Society
File Size	442277
File Format	PDF
ISSN	00983063
Volume Number	55
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Energy management Power system modeling Power system management Predictive models History Random processes Markov processes Delay effects Neural networks Time series analysis Neural Network, Hurst Exponent Dynamic Power Management, Time Delay
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Media Technology

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