NDLI: GMDH based auto-regressive model for China's energy consumption prediction

Content Provider	IEEE Xplore Digital Library
Author	Jin Xiao Haiyan Sun Yi Hu Yi Xiao
Copyright Year	2015
Description	Author affiliation: Sch. of Inf. Manage., Central China Normal Univ., Wuhan, China (Yi Xiao) \|\| Sch. of Manage., Univ. of Chinese Acad. of Sci., Beijing, China (Yi Hu) \|\| Bus. Sch., Sichuan Univ., Chengdu, China (Jin Xiao; Haiyan Sun)
Abstract	It is very significant for us to predict future energy consumption accurately. As for China's energy consumption annual time series, the sample size is relatively small. This study combines the traditional auto-regressive model with group method of data handling (GMDH) suitable for small sample prediction, and proposes a novel GMDH based auto-regressive (GAR) model. This model can finish the modeling process in self-organized manner, including finding the optimal complexity model, determining the optimal auto-regressive order and estimating model parameters. Further, four different GAR models, AS-GAR, MR-GAR, SRMSE-GAR and SMAPE-GAR, are constructed according to different external criteria. We conduct empirical analysis on three energy consumption time series, including the total energy consumption, the total petroleum consumption and the total gas consumption. The results show that AS-GAR model has the best forecasting performance among the four GAR models, and it outperforms ARIMA model, BP neural network model, SVM regression model and GM (1, 1) model. Finally, we give the out of sample prediction from 2014 to 2020 by GAR model.
Sponsorship	IEEE
Starting Page	1
Ending Page	6
File Size	395820
Page Count	6
File Format	PDF
e-ISBN	9781479918911
DOI	10.1109/LISS.2015.7369754
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-27
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Energy consumption Time series analysis Neural networks Group method of data handling Predictive models Data models Energy demand Complexity theory Small sample prediction Auto-regressive
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	Mr. Siddhartha Mukherjee siddhartha@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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray 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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