NDLI: Modeling and forecasting hourly electric load by multiple linear regression with interactions

Content Provider	IEEE Xplore Digital Library
Author	Tao Hong Min Gui Baran, M.E. Willis, H.L.
Copyright Year	2010
Description	Author affiliation: Quanta Technology, LLC, Raleigh, NC 27607 USA (Tao Hong; Min Gui; Willis, H.L.) \|\| Electric & Computer Engineering Department, North Carolina State Univ., Raleigh, 27695, USA (Baran, M.E.)
Abstract	Short-term electric load modeling and forecasting has been intensively studied during the past 50 years. With the emerging development of smart grid technologies, demand side management (DSM) starts to attract the attention of electric utilities again. To perform a decent DSM, beyond when and how much the demand will be, the utilities are facing another question: why is the electricity being consumed? In other words, what are the factors driving the fluctuation of the electric load at a particular time period? Understanding this issue can also be beneficial for the electric load forecasting with the purpose of energy purchase. This paper proposes a modern treatment of a classic technique, multiple linear regression, to model the hourly demand and investigate the causality of the consumption of electric energy. Various interactions are discovered, discussed, tested, and interpreted in this paper. The proposed approach has been used to generate the 3-year hourly energy demand forecast for a US utility.
Starting Page	1
Ending Page	8
File Size	1637754
Page Count	8
File Format	PDF
ISBN	9781424465491
ISSN	19449925
e-ISBN	9781424465514
e-ISBN	9781424483570
DOI	10.1109/PES.2010.5589959
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load modeling Linear regression Load forecasting Humans Electricity Artificial neural networks Accuracy multiple linear regression load management load modeling
Content Type	Text
Resource Type	Article

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