IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v226y2018icp240-251.html
   My bibliography  Save this article

Distribution patterns of energy consumed in classified public buildings through the data mining process

Author

Listed:
  • Chen, Yibo
  • Wu, Jianzhong

Abstract

Reliable spatio-temporal distribution analysis of building energy consumption is a crucial basis of bottom-up regional energy models, especially when faced with uncertain information at the planning stage. In existing statistical models, the regional consumption levels were mostly identified based on a large amount of samples with multi-dimensional parameters, which are usually not available for developing countries. Pointing to this, the distribution features of regional energy consumption are explored in this paper based on the whole procedure of data mining, which consists of three parts namely pre-processing, information mining, and validation & application. In this process, 212 samples of classified public buildings in Beijing and 66 samples in Hangzhou are included. Firstly, the pre-processing is conducted stepwise aiming at processing the missing data and the abnormal data. Afterwards, the interdisciplinary Lorenz curve is introduced to transfer the scatters into regular curves with satisfied fitting goodness. Thus, empirical formulae are extracted to quantify the nonlinear distribution principles of individual EUIs along with the accumulative building area. Finally, the achieved empirical formulae of different building types are validated, and the application potential of the identified patterns is discussed aiming at the planning stage. Through data mining of the limited datasets, this paper attempts to identify the hidden distribution patterns of regional energy consumption, which enables the regional modeling.

Suggested Citation

  • Chen, Yibo & Wu, Jianzhong, 2018. "Distribution patterns of energy consumed in classified public buildings through the data mining process," Applied Energy, Elsevier, vol. 226(C), pages 240-251.
    • Handle: RePEc:eee:appene:v:226:y:2018:i:c:p:240-251
    DOI: 10.1016/j.apenergy.2018.05.123
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918308444
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.05.123?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gastwirth, Joseph L & Glauberman, Marcia, 1976. "The Interpolation of the Lorenz Curve and Gini Index from Grouped Data," Econometrica, Econometric Society, vol. 44(3), pages 479-483, May.
    2. Brownsword, R.A. & Fleming, P.D. & Powell, J.C. & Pearsall, N., 2005. "Sustainable cities - modelling urban energy supply and demand," Applied Energy, Elsevier, vol. 82(2), pages 167-180, October.
    3. Olivo, Y. & Hamidi, A. & Ramamurthy, P., 2017. "Spatiotemporal variability in building energy use in New York City," Energy, Elsevier, vol. 141(C), pages 1393-1401.
    4. Constantine Kontokosta, 2015. "A Market-Specific Methodology for a Commercial Building Energy Performance Index," The Journal of Real Estate Finance and Economics, Springer, vol. 51(2), pages 288-316, August.
    5. Yang, Tianren & Zhang, Xiaoling, 2016. "Benchmarking the building energy consumption and solar energy trade-offs of residential neighborhoods on Chongming Eco-Island, China," Applied Energy, Elsevier, vol. 180(C), pages 792-799.
    6. Gastwirth, Joseph L, 1971. "A General Definition of the Lorenz Curve," Econometrica, Econometric Society, vol. 39(6), pages 1037-1039, November.
    7. Jacobson, Arne & Milman, Anita D. & Kammen, Daniel M., 2005. "Letting the (energy) Gini out of the bottle: Lorenz curves of cumulative electricity consumption and Gini coefficients as metrics of energy distribution and equity," Energy Policy, Elsevier, vol. 33(14), pages 1825-1832, September.
    8. Kontokosta, Constantine E. & Tull, Christopher, 2017. "A data-driven predictive model of city-scale energy use in buildings," Applied Energy, Elsevier, vol. 197(C), pages 303-317.
    9. Bennett, M. & Newborough, M., 2001. "Auditing energy use in cities," Energy Policy, Elsevier, vol. 29(2), pages 125-134, January.
    10. Luo, Xuan & Hong, Tianzhen & Chen, Yixing & Piette, Mary Ann, 2017. "Electric load shape benchmarking for small- and medium-sized commercial buildings," Applied Energy, Elsevier, vol. 204(C), pages 715-725.
    11. Zhang, Sufang & Jiao, Yiqian & Chen, Wenjun, 2017. "Demand-side management (DSM) in the context of China's on-going power sector reform," Energy Policy, Elsevier, vol. 100(C), pages 1-8.
    12. Zhao, Jing & Xin, Yajuan & Tong, Dingding, 2012. "Energy consumption quota of public buildings based on statistical analysis," Energy Policy, Elsevier, vol. 43(C), pages 362-370.
    13. Garshasbi, Samira & Kurnitski, Jarek & Mohammadi, Yousef, 2016. "A hybrid Genetic Algorithm and Monte Carlo simulation approach to predict hourly energy consumption and generation by a cluster of Net Zero Energy Buildings," Applied Energy, Elsevier, vol. 179(C), pages 626-637.
    14. Chalal, Moulay Larbi & Benachir, Medjdoub & White, Michael & Shrahily, Raid, 2016. "Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 761-776.
    15. Burleyson, Casey D. & Voisin, Nathalie & Taylor, Z. Todd & Xie, Yulong & Kraucunas, Ian, 2018. "Simulated building energy demand biases resulting from the use of representative weather stations," Applied Energy, Elsevier, vol. 209(C), pages 516-528.
    16. Mischke, Peggy & Karlsson, Kenneth B., 2014. "Modelling tools to evaluate China's future energy system – A review of the Chinese perspective," Energy, Elsevier, vol. 69(C), pages 132-143.
    17. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xu, Tong & Zhang, Yajing & Shi, Longyu & Feng, Yunshuang & Ke, Xinjue & Zhang, Chengliang, 2023. "A comprehensive evaluation framework of energy and resources consumption of public buildings: Case study, People's Bank of China," Applied Energy, Elsevier, vol. 351(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    2. Kontokosta, Constantine E. & Tull, Christopher, 2017. "A data-driven predictive model of city-scale energy use in buildings," Applied Energy, Elsevier, vol. 197(C), pages 303-317.
    3. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    4. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    5. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    6. Masato Okamoto, 2014. "Interpolating the Lorenz Curve: Methods to Preserve Shape and Remain Consistent with the Concentration Curves for Components," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(2), pages 349-384, June.
    7. Radulovic, Dusko & Skok, Srdjan & Kirincic, Vedran, 2011. "Energy efficiency public lighting management in the cities," Energy, Elsevier, vol. 36(4), pages 1908-1915.
    8. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    9. Ye, Zhongnan & Cheng, Kuangly & Hsu, Shu-Chien & Wei, Hsi-Hsien & Cheung, Clara Man, 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach," Applied Energy, Elsevier, vol. 301(C).
    10. Alfred Ultsch & Jörn Lötsch, 2015. "Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-15, June.
    11. Jacqueline Hettel Tidwell & Abraham Tidwell & Steffan Nelson, 2018. "Surveying the Solar Power Gap: Assessing the Spatial Distribution of Emerging Photovoltaic Solar Adoption in the State of Georgia, U.S.A," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    12. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    13. Rafiee, A. & Dias, E. & Koomen, E., 2019. "Analysing the impact of spatial context on the heat consumption of individual households," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 461-470.
    14. Jungsik Choi & Sejin Lee, 2023. "A Suggestion of the Alternatives Evaluation Method through IFC-Based Building Energy Performance Analysis," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    15. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
    16. (Giancarlo) Falcocchio, John C. & Malik, Awais & Kontokosta, Constantine E., 2018. "A data-driven methodology for equitable value-capture financing of public transit operations and maintenance," Transport Policy, Elsevier, vol. 66(C), pages 107-115.
    17. Beach, Charles M., 1979. "Model-Free Statistical Inference with Lorenz Curves, Income Shares, and Gini Coefficients," Queen's Institute for Economic Research Discussion Papers 275154, Queen's University - Department of Economics.
    18. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    19. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
    20. Tomasz Szul & Stanisław Kokoszka, 2020. "Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization," Energies, MDPI, vol. 13(6), pages 1-17, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:226:y:2018:i:c:p:240-251. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.