skip to main content
10.1145/3371425.3371493acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaiipccConference Proceedingsconference-collections
research-article

Research on energy consumption mode of green culture complex based on data mining technology

Authors Info & Claims
Published:19 December 2019Publication History

ABSTRACT

Based on the operation data of the Green Culture Complex, this paper excavates and analyses its data. Data mining is carried out for the lighting and socket energy consumption data of the Library. Firstly, the monotone sequential logic detection algorithm is used to detect the abnormal data, and the mean complement method is used to process the abnormal data. Finally, R-type clustering method is proposed to analyze the mode of running energy consumption. The results obtained have a high degree of agreement with the actual operation effect, which has a certain guiding significance for determining the applicable operation control mode of the building in the future.

References

  1. Zhang X, Qu C, Huang B, et al. (2016). Exception analysis and treatment of operation data in green building. 2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE), IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  2. Zhang F, Mao Z and Ding G (2016). Simulation and Analysis of Classification Optimization model of Temperature Sensing Big Data in Intelligent Building. The, Multidisciplinary International Social Networks Conference on Socialinformatics, Data Science, ACM, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Han J and Kamber M (2015). Data mining: concepts and techniques. The Morgan Kaufmann series in data management systems. Antimicrobial Agents & Chemotherapy, 59(3), 1435--40.Google ScholarGoogle Scholar
  4. Zhang S C, Zhang C Q and Yang Q (2003). Data preparation for data mining, Applied Artificial Intelligence, 17(2003), 375--381.Google ScholarGoogle ScholarCross RefCross Ref
  5. Kusiak A, Tang F and Xu G (2011). Multi-objective optimization of HVAC system with an evolutionary computation algorithm. Energy, 36(5), 2440--2449.Google ScholarGoogle ScholarCross RefCross Ref
  6. Yu Z, Haghighat F, Fung B C M, et al. (2012). A novel methodology for knowledge discovery through mining associations between building operational data. Energy & Buildings, 47(47), 430--440.Google ScholarGoogle ScholarCross RefCross Ref
  7. M R Amin-Naseri and A R Soroush (2008). Combined use of unsupervised and supervised learning for daily peak load forecasting. Energy Conversion and Management, 49(2008), 1302--1308.Google ScholarGoogle ScholarCross RefCross Ref
  8. D F M Cabrera and H Zareipour (2013). Data association mining for identifying lighting energy waste patterns in educational institutes. Energy and Buildings Google ScholarGoogle ScholarCross RefCross Ref
  9. Huang Botao (2016). Data Analysis of Green Building Based on Integration Platform of Intelligent System. Nankai University.Google ScholarGoogle Scholar
  10. Zhang Chengping (2006). Filling of incomplete data. Central South University.Google ScholarGoogle Scholar

Index Terms

  1. Research on energy consumption mode of green culture complex based on data mining technology

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      AIIPCC '19: Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing
      December 2019
      464 pages
      ISBN:9781450376334
      DOI:10.1145/3371425

      Copyright © 2019 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 19 December 2019

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      AIIPCC '19 Paper Acceptance Rate78of211submissions,37%Overall Acceptance Rate78of211submissions,37%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader