Research on the Correlation of Monthly Electricity ... › publication › fulltext › Research-... › publication › fulltext › Research-...by L Zhang · 2018 · Cited by 2 · Related articlesThe result is shown in the figure below. Fig. 1. Correlation network
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Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science 139 (2018) 496–503 Procedia Computer Science 00 (2018) 000–000
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The International Academy of Information Technology and Quantitative Management, the Peter Kiewit Institute,Technology University of Nebraska The International Academy of Information and Quantitative Management, the Peter Kiewit Institute, University of Nebraska
Research on the Correlation of Monthly Electricity Consumption Research on the Correlation Monthly in Different Industries: AofCase StudyElectricity of BazhouConsumption County a b bof Bazhou c,d,f County in Different Industries: A Case Study Luhua Zhang , Zili Huang , Zhengze Li , Kun Guo Luhua Zhanga, Zili Huangb, Zhengze Lib, Kun Guoc,d,f
Jibei Electric Power Company Limited Metering Centre, Beijing 102208, China b University of Political Science and Law, Beijing, 102449, China JibeiChina Electric Power Company Limited Metering Centre, Beijing 102208, China c School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China b China University of Political Science and Law, Beijing, 102449, China d Research Centre on Fictitious Economy & Data Science, UCAS, Beijing,100190, China c Schoolf of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China CAS Key Laboratory of Big Data Mining and Knowledge Management, Beijing, 100190, Chin d Research Centre on Fictitious Economy & Data Science, UCAS, Beijing,100190, China f CAS Key Laboratory of Big Data Mining and Knowledge Management, Beijing, 100190, Chin a
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Abstract Abstract
In recent years, with the rapid economic growth, the electric power consumption has led to a continuous increase. Besides, adapt to the and demand relationships, industrial structurehas hasled changed dramatically, and In recent to years, with thenew rapidsupply economic growth, the electric power consumption to a continuous increase. electricity consumption in different industries has changed accordingly, such as in Hebei province that boasts Besides, to adapt to the new supply and demand relationships, industrial structure has changed dramatically, anda large amount of traditional industries. Hence,has thechanged study ofaccordingly, the change such of electricity consumption in different electricity consumption in different industries as in Hebei province that boasts a industries andofthe correlation betweenHence, them the have great forconsumption monitoring in changes in large amount traditional industries. study of practical the changesignificance of electricity different macroeconomic structure and aiding decision-making in power companies. This paper a typical county industries and the correlation between them have great practical significance for selected monitoring changes in in Hebei Province and used method of complex network andcompanies. sensitive coefficient analyzea the correlation macroeconomic structure andtheaiding decision-making in power This papertoselected typical county of electricity load among different industries. This paper found that the industrial structure in Bazhou county in Hebei Province and used the method of complex network and sensitive coefficient to analyze the correlation show a strongload relationship with the industries. traditional This industry classification. Theindustrial correlation betweenineach industry in of electricity among different paper found that the structure Bazhou county the same traditional industrywith is stronger and more stable, and it is weakerThe andcorrelation more changeable regarding different show a strong relationship the traditional industry classification. between each industry in traditional industries. the same traditional industry is stronger and more stable, and it is weaker and more changeable regarding different traditional industries.
© 2018 The Authors. Published by Elsevier B.V. © 2018 The Authors. by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license Peer review under responsibility of the scientific committee of (http://creativecommons.org/licenses/by-nc-nd/4.0/) The International Academy of Information Technology and This is an open access article underofthe CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility the scientific committee of The International Academy of Information Technology Quantitative Management, the Peter Kiewit Institute, University of Nebraska. PeerQuantitative review underManagement, responsibilitytheofPeter the scientific committee of The International and Kiewit Institute, University of Nebraska. Academy of Information Technology and Quantitative Management, the Peter Kiewit Institute, University of Nebraska. Keywords: Power Consumption; Key Industry; Complex Networks; Sensetive Analysis
Keywords: Power Consumpti
