IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i11p2119-d119321.html
   My bibliography  Save this article

In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

Author

Listed:
  • Byeongjoon Noh

    (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

  • Juntae Son

    (School of Planning, Design, and Construction, Michigan State University, 552 W. Circle Dr., East Lansing, MI 48824, USA)

  • Hansaem Park

    (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

  • Seongju Chang

    (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

Abstract

Significant amounts of energy are consumed in the commercial building sector, resulting in various adverse environmental issues. To reduce energy consumption and improve energy efficiency in commercial buildings, it is necessary to develop effective methods for analyzing building energy use. In this study, we propose a data cube model combined with association rule mining for more flexible and detailed analysis of building energy consumption profiles using the Commercial Buildings Energy Consumption Survey (CBECS) dataset, which has accumulated over 6700 existing commercial buildings across the U.S.A. Based on the data cube model, a multidimensional commercial sector building energy analysis was performed based upon on-line analytical processing (OLAP) operations to assess the energy efficiency according to building factors with various levels of abstraction. Furthermore, the proposed analysis system provided useful information that represented a set of energy efficient combinations by applying the association rule mining method. We validated the feasibility and applicability of the proposed analysis model by structuring a building energy analysis system and applying it to different building types, weather conditions, composite materials, and heating/cooling systems of the multitude of commercial buildings classified in the CBECS dataset.

Suggested Citation

  • Byeongjoon Noh & Juntae Son & Hansaem Park & Seongju Chang, 2017. "In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining," Sustainability, MDPI, vol. 9(11), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:11:p:2119-:d:119321
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/11/2119/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/11/2119/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dongjun Suh & Seongju Chang, 2014. "A Heuristic Rule-Based Passive Design Decision Model for Reducing Heating Energy Consumption of Korean Apartment Buildings," Energies, MDPI, vol. 7(11), pages 1-33, October.
    2. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    3. Lei, Jiawei & Yang, Jinglei & Yang, En-Hua, 2016. "Energy performance of building envelopes integrated with phase change materials for cooling load reduction in tropical Singapore," Applied Energy, Elsevier, vol. 162(C), pages 207-217.
    4. Mathew, Paul A. & Dunn, Laurel N. & Sohn, Michael D. & Mercado, Andrea & Custudio, Claudine & Walter, Travis, 2015. "Big-data for building energy performance: Lessons from assembling a very large national database of building energy use," Applied Energy, Elsevier, vol. 140(C), pages 85-93.
    5. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    6. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    7. Kahn, Matthew E. & Kok, Nils & Quigley, John M., 2014. "Carbon emissions from the commercial building sector: The role of climate, quality, and incentives," Journal of Public Economics, Elsevier, vol. 113(C), pages 1-12.
    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. Feifeng Jiang & Kwok Kit Richard Yuen & Eric Wai Ming Lee & Jun Ma, 2020. "Analysis of Run-Off-Road Accidents by Association Rule Mining and Geographic Information System Techniques on Imbalanced Datasets," Sustainability, MDPI, vol. 12(12), pages 1-32, June.
    2. Guo, Hongshan & Ferrara, Maria & Coleman, James & Loyola, Mauricio & Meggers, Forrest, 2020. "Simulation and measurement of air temperatures and mean radiant temperatures in a radiantly heated indoor space," Energy, Elsevier, vol. 193(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. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    2. Sun, Yanyi & Wilson, Robin & Wu, Yupeng, 2018. "A Review of Transparent Insulation Material (TIM) for building energy saving and daylight comfort," Applied Energy, Elsevier, vol. 226(C), pages 713-729.
    3. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    4. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    5. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    6. Mingli Li & Guoqing Gui & Zhibin Lin & Long Jiang & Hong Pan & Xingyu Wang, 2018. "Numerical Thermal Characterization and Performance Metrics of Building Envelopes Containing Phase Change Materials for Energy-Efficient Buildings," Sustainability, MDPI, vol. 10(8), pages 1-23, July.
    7. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    8. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    9. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    10. Moaaz Elkabalawy & Abobakr Al-Sakkaf & Eslam Mohammed Abdelkader & Ghasan Alfalah, 2024. "CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors," Sustainability, MDPI, vol. 16(17), pages 1-21, August.
    11. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    12. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    13. Xiao, Lan & Qin, Liang-Liang & Wu, Shuang-Ying, 2023. "Effect of PV-Trombe wall in the multi-storey building on standard effective temperature (SET)-based indoor thermal comfort," Energy, Elsevier, vol. 263(PB).
    14. Bimaganbetova, Madina & Memon, Shazim Ali & Sheriyev, Almas, 2020. "Performance evaluation of phase change materials suitable for cities representing the whole tropical savanna climate region," Renewable Energy, Elsevier, vol. 148(C), pages 402-416.
    15. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    16. Song, Kwonsik & Kim, Sooyoung & Park, Moonseo & Lee, Hyun-Soo, 2017. "Energy efficiency-based course timetabling for university buildings," Energy, Elsevier, vol. 139(C), pages 394-405.
    17. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    18. Chegut, Andrea & Eichholtz, Piet & Kok, Nils, 2019. "The price of innovation: An analysis of the marginal cost of green buildings," Journal of Environmental Economics and Management, Elsevier, vol. 98(C).
    19. Mao, Ning & Pan, Dongmei & Li, Zhao & Xu, Yingjie & Song, Mengjie & Deng, Shiming, 2017. "A numerical study on influences of building envelope heat gain on operating performances of a bed-based task/ambient air conditioning (TAC) system in energy saving and thermal comfort," Applied Energy, Elsevier, vol. 192(C), pages 213-221.
    20. Eichholtz, Piet & Holtermans, Rogier & Kok, Nils & Yönder, Erkan, 2019. "Environmental performance and the cost of debt: Evidence from commercial mortgages and REIT bonds," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 19-32.

    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:gam:jsusta:v:9:y:2017:i:11:p:2119-:d:119321. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.