Energy efficiency evaluation method based on deep learning model

Meng Fansheng; Li Bin; Zenglei Yue; Cheng Jiangnan; Liu Zhi; Wan Jie

doi:10.1109/IMCEC.2016.7867444

Energy efficiency evaluation method based on deep learning model

Meng Fansheng
, Li Bin
, Zenglei Yue
, Cheng Jiangnan
, Liu Zhi
, Wan Jie

Harbin Engineering University
Heilongjiang Science and Technology Information Research Institute
Harbin Ranzhuo Technology Co. Ltd
School of Energy Science and Engineering, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Energy efficiency measurement and its influence factors is important way of energy efficiency evaluation. In this paper, character identification method has been proposed to determine influence factors of energy efficiency and energy efficiency of 24 provinces in china is analyzed and evaluated by deep learning method. By comparison, two classification and prediction models are built with two other common classification and prediction algorithms. Case study with collected data revealed that the classification accuracy of three model is all over 90% and the deep learning model shown the best results. And then energy efficiency of other six provinces are predicted with three model and the deep learning model shown the best results. In the end, a strategy is put forward to improve Chinese energy efficiency.

Original language	English
Title of host publication	Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016
Editors	Bing Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1403-1407
Number of pages	5
ISBN (Electronic)	9781467396127
DOIs	https://doi.org/10.1109/IMCEC.2016.7867444
State	Published - 28 Feb 2017
Externally published	Yes
Event	2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016 - Xi'an, China Duration: 3 Oct 2016 → 5 Oct 2016

Publication series

Name	Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016

Conference

Conference	2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016
Country/Territory	China
City	Xi'an
Period	3/10/16 → 5/10/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Classification
Cluster
Data mining
Energy efficiency
Feature selection

Access to Document

10.1109/IMCEC.2016.7867444

Cite this

Fansheng, M., Bin, L., Yue, Z., Jiangnan, C., Zhi, L., & Jie, W. (2017). Energy efficiency evaluation method based on deep learning model. In B. Xu (Ed.), Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016 (pp. 1403-1407). Article 7867444 (Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IMCEC.2016.7867444

Fansheng, Meng ; Bin, Li ; Yue, Zenglei et al. / Energy efficiency evaluation method based on deep learning model. Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016. editor / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1403-1407 (Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016).

@inproceedings{08bd0ada52ed459495e273e18b02b782,

title = "Energy efficiency evaluation method based on deep learning model",

abstract = "Energy efficiency measurement and its influence factors is important way of energy efficiency evaluation. In this paper, character identification method has been proposed to determine influence factors of energy efficiency and energy efficiency of 24 provinces in china is analyzed and evaluated by deep learning method. By comparison, two classification and prediction models are built with two other common classification and prediction algorithms. Case study with collected data revealed that the classification accuracy of three model is all over 90\% and the deep learning model shown the best results. And then energy efficiency of other six provinces are predicted with three model and the deep learning model shown the best results. In the end, a strategy is put forward to improve Chinese energy efficiency.",

keywords = "Classification, Cluster, Data mining, Energy efficiency, Feature selection",

author = "Meng Fansheng and Li Bin and Zenglei Yue and Cheng Jiangnan and Liu Zhi and Wan Jie",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016 ; Conference date: 03-10-2016 Through 05-10-2016",

year = "2017",

month = feb,

day = "28",

doi = "10.1109/IMCEC.2016.7867444",

language = "英语",

series = "Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1403--1407",

editor = "Bing Xu",

booktitle = "Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016",

address = "美国",

}

Fansheng, M, Bin, L, Yue, Z, Jiangnan, C, Zhi, L & Jie, W 2017, Energy efficiency evaluation method based on deep learning model. in B Xu (ed.), Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016., 7867444, Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1403-1407, 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016, Xi'an, China, 3/10/16. https://doi.org/10.1109/IMCEC.2016.7867444

Energy efficiency evaluation method based on deep learning model. / Fansheng, Meng; Bin, Li; Yue, Zenglei et al.
Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1403-1407 7867444 (Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Energy efficiency evaluation method based on deep learning model

AU - Fansheng, Meng

AU - Bin, Li

AU - Yue, Zenglei

AU - Jiangnan, Cheng

AU - Zhi, Liu

AU - Jie, Wan

PY - 2017/2/28

Y1 - 2017/2/28

N2 - Energy efficiency measurement and its influence factors is important way of energy efficiency evaluation. In this paper, character identification method has been proposed to determine influence factors of energy efficiency and energy efficiency of 24 provinces in china is analyzed and evaluated by deep learning method. By comparison, two classification and prediction models are built with two other common classification and prediction algorithms. Case study with collected data revealed that the classification accuracy of three model is all over 90% and the deep learning model shown the best results. And then energy efficiency of other six provinces are predicted with three model and the deep learning model shown the best results. In the end, a strategy is put forward to improve Chinese energy efficiency.

AB - Energy efficiency measurement and its influence factors is important way of energy efficiency evaluation. In this paper, character identification method has been proposed to determine influence factors of energy efficiency and energy efficiency of 24 provinces in china is analyzed and evaluated by deep learning method. By comparison, two classification and prediction models are built with two other common classification and prediction algorithms. Case study with collected data revealed that the classification accuracy of three model is all over 90% and the deep learning model shown the best results. And then energy efficiency of other six provinces are predicted with three model and the deep learning model shown the best results. In the end, a strategy is put forward to improve Chinese energy efficiency.

KW - Classification

KW - Cluster

KW - Data mining

KW - Energy efficiency

KW - Feature selection

UR - https://www.scopus.com/pages/publications/85016823729

U2 - 10.1109/IMCEC.2016.7867444

DO - 10.1109/IMCEC.2016.7867444

M3 - 会议稿件

AN - SCOPUS:85016823729

T3 - Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016

SP - 1403

EP - 1407

BT - Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016

A2 - Xu, Bing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016

Y2 - 3 October 2016 through 5 October 2016

ER -

Fansheng M, Bin L, Yue Z, Jiangnan C, Zhi L, Jie W. Energy efficiency evaluation method based on deep learning model. In Xu B, editor, Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1403-1407. 7867444. (Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016). doi: 10.1109/IMCEC.2016.7867444

Energy efficiency evaluation method based on deep learning model

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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