Big data-informed energy efficiency assessment of China industry sectors based on K-means clustering

被引：61

作者：

Liu, Gengyuan ^{[1
,2
]}

Yang, Jin ^{[3
]}

Hao, Yan ^{[1
]}

Zhang, Yan ^{[1
,2
]}

机构：

[1] Beijing Normal Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100875, Peoples R China

[2] Beijing Engn Res Ctr Watershed Environm Restorat, Beijing 100875, Peoples R China

[3] China Univ Geosci, Sch Humanities & Econ Management, Beijing 100083, Peoples R China

来源：

JOURNAL OF CLEANER PRODUCTION | 2018年 / 183卷

基金：

中国国家自然科学基金;

关键词：

Big-data; Energy efficiency assessment; K-means; Multi-dimension association rules; MODEL;

D O I：

10.1016/j.jclepro.2018.02.129

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The regional energy management body has a large amount of regional industrial companies' energy consumption data. It can evaluate the energy utilization of listed regional industrial companies based on the total data and, then, find the key points for understanding the resources usage patterns, identifying the problematic companies, and establishing good energy consumption practices. This paper reviews the research progress on big data analysis and industrial energy efficiency evaluation and focuses on the energy efficiency evaluation methods based on energy consumption process analysis and big data mining approach. Based on K-means and multi-dimensional association rules algorithm, to analyze the characteristics of regional energy consumption in different industries and companies, we cluster single industry in K-means and finding their levels of water and energy consumption. This classification provided us a reference point to identify the industries and companies to focus on and locate the bad consumption practices and environmental performance. Then, multi-dimensional association rules are used to find the correlation of processes, companies and energy efficiency to guide the energy conservation in regional energy monitor. The output of our research is a working Big Data analytics platform and the results generated from advance analytics techniques applied specifically to solve regional energy efficiency problems. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：304 / 314

页数：11

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