IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3189-d373724.html
   My bibliography  Save this article

Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data

Author

Listed:
  • Sukjoon Oh

    (CAES Energy Efficiency Research Institute, Mechanical and Biomedical Engineering, Boise State University, Boise, ID 83725, USA)

  • Chul Kim

    (Department of Architecture, Texas A&M University, College Station, TX 77840, USA)

  • Joonghyeok Heo

    (Department of Geosciences, University of Texas-Permian Basin, Odessa, TX 79762, USA)

  • Sung Lok Do

    (Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea)

  • Kee Han Kim

    (Department of Architectural Engineering, University of Ulsan, Ulsan 44610, Korea)

Abstract

Many smart apartments and renovated residential buildings have installed Smart Meters (SMs), which collect interval data to accelerate more efficient energy management in multi-family residential buildings. SMs are widely used for electricity, but many utility companies have been working on systems for natural gas and water monitoring to be included in SMs. In this study, we analyze heating energy use data obtained from SMs for short-term monitoring and annual predictions using change-point models for the coefficient checking method. It was found that 9-month periods were required to search the best short-term heating energy monitoring periods when non-weather-related and weather-related heating loads and heating change-point temperatures are considered. In addition, the 9-month to 11-month periods were needed for the analysis to apply to other case study residences in the same high-rise apartment. For the accurate annual heating prediction, 11-month periods were necessary. Finally, the results from the heating performance analysis of this study were compared with the cooling performance analysis from a previous study. This study found that the coefficient checking method is a simple and easy-to-interpret approach to analyze interval heating energy use in multi-family residential buildings. It was also found that the period of short-term energy monitoring should be carefully selected to effectively collect targeted heating and cooling data for an energy audit or annual prediction.

Suggested Citation

  • Sukjoon Oh & Chul Kim & Joonghyeok Heo & Sung Lok Do & Kee Han Kim, 2020. "Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data," Energies, MDPI, vol. 13(12), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3189-:d:373724
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3189/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/12/3189/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gde Dharma Nugraha & Ardiansyah Musa & Jaiyoung Cho & Kishik Park & Deokjai Choi, 2018. "Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings," Energies, MDPI, vol. 11(4), pages 1-20, March.
    2. Jiyang Wang & Yuyang Gao & Xuejun Chen, 2018. "A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 11(6), pages 1-30, June.
    3. Fan, Cheng & Xiao, Fu & Yan, Chengchu & Liu, Chengliang & Li, Zhengdao & Wang, Jiayuan, 2019. "A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning," Applied Energy, Elsevier, vol. 235(C), pages 1551-1560.
    4. Michel Noussan & Benedetto Nastasi, 2018. "Data Analysis of Heating Systems for Buildings—A Tool for Energy Planning, Policies and Systems Simulation," Energies, MDPI, vol. 11(1), pages 1-15, January.
    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. Khan Rahmat Ullah & Marudhappan Thirugnanasambandam & Rahman Saidur & Kazi Akikur Rahman & Md. Riaz Kayser, 2021. "Analysis of Energy Use and Energy Savings: A Case Study of a Condiment Industry in India," Energies, MDPI, vol. 14(16), pages 1-25, August.
    2. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.

    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. Lo Basso, Gianluigi & de Santoli, Livio & Paiolo, Romano & Losi, Claudio, 2021. "The potential role of trans-critical CO2 heat pumps within a solar cooling system for building services: The hybridised system energy analysis by a dynamic simulation model," Renewable Energy, Elsevier, vol. 164(C), pages 472-490.
    2. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    3. Jia, Kunqi & Guo, Ge & Xiao, Jucheng & Zhou, Huan & Wang, Zhihua & He, Guangyu, 2019. "Data compression approach for the home energy management system," Applied Energy, Elsevier, vol. 247(C), pages 643-656.
    4. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    5. Ali Elkamel, 2018. "Energy Production Systems," Energies, MDPI, vol. 11(10), pages 1-4, September.
    6. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    7. Lifang Zhang & Jianzhou Wang & Zhenkun Liu, 2023. "Power grid operation optimization and forecasting using a combined forecasting system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 124-153, January.
    8. Liang, Xinbin & Zhu, Xu & Chen, Siliang & Jin, Xinqiao & Xiao, Fu & Du, Zhimin, 2023. "Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios," Applied Energy, Elsevier, vol. 349(C).
    9. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Massidda, Luca & Marrocu, Marino, 2023. "Total and thermal load forecasting in residential communities through probabilistic methods and causal machine learning," Applied Energy, Elsevier, vol. 351(C).
    11. Jeeyoung Lim & Joseph J. Kim & Sunkuk Kim, 2021. "A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    12. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
    13. Bellocchi, Sara & Manno, Michele & Noussan, Michel & Prina, Matteo Giacomo & Vellini, Michela, 2020. "Electrification of transport and residential heating sectors in support of renewable penetration: Scenarios for the Italian energy system," Energy, Elsevier, vol. 196(C).
    14. Cai, Qingsen & Luo, XingQi & Wang, Peng & Gao, Chunyang & Zhao, Peiyu, 2022. "Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application," Applied Energy, Elsevier, vol. 305(C).
    15. Umberto Berardi & Lamberto Tronchin & Massimiliano Manfren & Benedetto Nastasi, 2018. "On the Effects of Variation of Thermal Conductivity in Buildings in the Italian Construction Sector," Energies, MDPI, vol. 11(4), pages 1-17, April.
    16. Matteo Giacomo Prina & Giampaolo Manzolini & David Moser & Roberto Vaccaro & Wolfram Sparber, 2020. "Multi-Objective Optimization Model EPLANopt for Energy Transition Analysis and Comparison with Climate-Change Scenarios," Energies, MDPI, vol. 13(12), pages 1-22, June.
    17. Chongchong Xu & Zhicheng Liao & Chaojie Li & Xiaojun Zhou & Renyou Xie, 2022. "Review on Interpretable Machine Learning in Smart Grid," Energies, MDPI, vol. 15(12), pages 1-31, June.
    18. Xin Tan & Penglin Zhang & Junqiang Wang & Jiewen Hong, 2019. "Research on Urban Bearing Capacity of Gas Supply Stations," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    19. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    20. Simon Pezzutto & Silvia Croce & Stefano Zambotti & Lukas Kranzl & Antonio Novelli & Pietro Zambelli, 2019. "Assessment of the Space Heating and Domestic Hot Water Market in Europe—Open Data and Results," Energies, MDPI, vol. 12(9), pages 1-16, May.

    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:jeners:v:13:y:2020:i:12:p:3189-:d:373724. 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.