NDLI: Forecasting energy trends and peak usage at the University of Virginia

Content Provider	IEEE Xplore Digital Library
Author	Heylman, C. Young Gee Kim Jiaqi Wang
Copyright Year	2015
Description	Author affiliation: Univ. of Virginia, Charlottesville, VA, USA (Heylman, C.; Young Gee Kim; Jiaqi Wang)
Abstract	Forecasting energy trends, especially peak usage, is a valuable and necessary part of energy management. Accurate prediction allows for the control and alleviation of overuse during peak times with the implementation of energy efficiencies. Using hourly kilowatt data from over 200 buildings on the University of Virginia's campus this paper examines the most effective techniques for developing both individual building and overall grid use energy models with a specific focus on predetermining peak usage points. This paper proposes that the expectation-maximization algorithm within the state-space framework is the most effective method for smoothing missing values, a common occurrence when working with metered energy data. Next, this paper covers two separate methods for creating forecasting models. The first, a linear model, was found to be most successful in predicting campuswide energy usage, with the inclusion of features of temperature, humidity, school session, and three temporal variables. The second method, found to be most successful when forecasting short term individual building energy use, uses a seasonal autoregressive integrated moving average (SARIMA) model. Finally this paper delves into the intricacies involved in clustering buildings based on their energy usage trends rather than building use, and the implications that arise from such a process. The conclusions made in this paper can be rescaled and applied to larger energy systems outside the university setting.
Starting Page	362
Ending Page	368
File Size	394187
Page Count	7
File Format	PDF
e-ISBN	9781479918324
DOI	10.1109/SIEDS.2015.7117006
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Buildings Predictive models Forecasting Humidity Market research Load modeling Linear regression Linear Regression ARIMA Data Science Energy K-Means Clustering
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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