IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v70y2017icp598-609.html
   My bibliography  Save this article

Data science for building energy management: A review

Author

Listed:
  • Molina-Solana, Miguel
  • Ros, María
  • Ruiz, M. Dolores
  • Gómez-Romero, Juan
  • Martin-Bautista, M.J.

Abstract

The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

Suggested Citation

  • Molina-Solana, Miguel & Ros, María & Ruiz, M. Dolores & Gómez-Romero, Juan & Martin-Bautista, M.J., 2017. "Data science for building energy management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 598-609.
  • Handle: RePEc:eee:rensus:v:70:y:2017:i:c:p:598-609
    DOI: 10.1016/j.rser.2016.11.132
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032116308814
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2016.11.132?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. Zhou, Kai-le & Yang, Shan-lin & Shen, Chao, 2013. "A review of electric load classification in smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 103-110.
    2. Kusiak, Andrew & Li, Mingyang & Zhang, Zijun, 2010. "A data-driven approach for steam load prediction in buildings," Applied Energy, Elsevier, vol. 87(3), pages 925-933, March.
    3. Stergiopoulos, George & Kotzanikolaou, Panayiotis & Theocharidou, Marianthi & Gritzalis, Dimitris, 2015. "Risk mitigation strategies for critical infrastructures based on graph centrality analysis," International Journal of Critical Infrastructure Protection, Elsevier, vol. 10(C), pages 34-44.
    4. Kusiak, Andrew & Li, Mingyang & Tang, Fan, 2010. "Modeling and optimization of HVAC energy consumption," Applied Energy, Elsevier, vol. 87(10), pages 3092-3102, October.
    5. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    6. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
    7. Vaghefi, A. & Jafari, M.A. & Bisse, Emmanuel & Lu, Y. & Brouwer, J., 2014. "Modeling and forecasting of cooling and electricity load demand," Applied Energy, Elsevier, vol. 136(C), pages 186-196.
    Full references (including those not matched with items on IDEAS)

    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. Le Cam, M. & Daoud, A. & Zmeureanu, R., 2016. "Forecasting electric demand of supply fan using data mining techniques," Energy, Elsevier, vol. 101(C), pages 541-557.
    2. Cui, Can & Zhang, Xin & Cai, Wenjian, 2020. "An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model," Applied Energy, Elsevier, vol. 264(C).
    3. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    4. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    5. Hyunsoo Kim & Jiseok Jeong & Changwan Kim, 2022. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    6. Liu, Che & Li, Fan & Zhang, Chenghui & Sun, Bo & Zhang, Guanguan, 2023. "A day-ahead prediction method for high-resolution electricity consumption in residential units," Energy, Elsevier, vol. 265(C).
    7. 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.
    8. Li, Guannan & Hu, Yunpeng & Chen, Huanxin & Li, Haorong & Hu, Min & Guo, Yabin & Liu, Jiangyan & Sun, Shaobo & Sun, Miao, 2017. "Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions," Applied Energy, Elsevier, vol. 185(P1), pages 846-861.
    9. Athanasiadis, C.L. & Papadopoulos, T.A. & Kryonidis, G.C. & Doukas, D.I., 2024. "A review of distribution network applications based on smart meter data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    10. 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).
    11. Pengwei Su & Xue Tian & Yan Wang & Shuai Deng & Jun Zhao & Qingsong An & Yongzhen Wang, 2017. "Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System," Energies, MDPI, vol. 10(9), pages 1-13, August.
    12. Tran, Duc-Hoc & Luong, Duc-Long & Chou, Jui-Sheng, 2020. "Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings," Energy, Elsevier, vol. 191(C).
    13. Kočí, Jan & Maděra, Jiří & Černý, Robert, 2015. "A fast computational approach for the determination of thermal properties of hollow bricks in energy-related calculations," Energy, Elsevier, vol. 83(C), pages 749-755.
    14. Robinson, Caleb & Dilkina, Bistra & Hubbs, Jeffrey & Zhang, Wenwen & Guhathakurta, Subhrajit & Brown, Marilyn A. & Pendyala, Ram M., 2017. "Machine learning approaches for estimating commercial building energy consumption," Applied Energy, Elsevier, vol. 208(C), pages 889-904.
    15. 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.
    16. Tomasz Szul & Sylwester Tabor & Krzysztof Pancerz, 2021. "Application of the BORUTA Algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating," Energies, MDPI, vol. 14(10), pages 1-13, May.
    17. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
    18. Kamel, Ehsan & Sheikh, Shaya & Huang, Xueqing, 2020. "Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days," Energy, Elsevier, vol. 206(C).
    19. Hribar, Rok & Potočnik, Primož & Šilc, Jurij & Papa, Gregor, 2019. "A comparison of models for forecasting the residential natural gas demand of an urban area," Energy, Elsevier, vol. 167(C), pages 511-522.
    20. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.

    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:rensus:v:70:y:2017:i:c:p:598-609. 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/600126/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.