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Apples or oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset

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  • Park, June Young
  • Yang, Xiya
  • Miller, Clayton
  • Arjunan, Pandarasamy
  • Nagy, Zoltan

Abstract

Buildings are responsible for 30–40% of the anthropogenic greenhouse gas emissions and energy consumption worldwide. Thus, reducing the overall energy use and associated emissions in buildings is crucial for meeting sustainability goals for the future. In recent years, smart energy meters have been deployed to enable monitoring of energy use data with hourly or sub-hourly temporal resolution. The concurrent rise of information technologies and data analytics enabled the development of novel applications such as customer segmentation, load profiling, demand response, energy forecasting and anomaly detection. In this paper, we address load profiling and benchmarking, i.e., determining peer groups for buildings. Traditionally, static characteristics, e.g., primary space use (PSU) together with the annual energy-use-intensity (EUI) have been used to compare the performance of buildings. Data-driven benchmarking approaches have begun to also consider the shape of the load profiles as a means for comparison. In this work, we identify three fundamental load shape profiles that characterize the temporal energy use in any building. We obtain this result by collecting a dataset of unprecedented variety in size (3829 buildings) and primary use (75 programs), and applying a rigorous clustering analysis followed by entropy calculation for each building. The existence of fundamental load shape profiles challenges the man-made, artificial classification of buildings. We demonstrate in a benchmarking application that the resulting data-driven groups are more homogeneous, and therefore more suitable for comparisons between buildings. Our findings have potential implications for portfolio management, building and urban energy simulations, demand response and renewable energy integration in buildings.

Suggested Citation

  • Park, June Young & Yang, Xiya & Miller, Clayton & Arjunan, Pandarasamy & Nagy, Zoltan, 2019. "Apples or oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset," Applied Energy, Elsevier, vol. 236(C), pages 1280-1295.
  • Handle: RePEc:eee:appene:v:236:y:2019:i:c:p:1280-1295
    DOI: 10.1016/j.apenergy.2018.12.025
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    References listed on IDEAS

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    1. Park, June Young & Nagy, Zoltan, 2018. "Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2664-2679.
    2. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
    3. 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.
    4. Park, Hyo Seon & Lee, Minhyun & Kang, Hyuna & Hong, Taehoon & Jeong, Jaewook, 2016. "Development of a new energy benchmark for improving the operational rating system of office buildings using various data-mining techniques," Applied Energy, Elsevier, vol. 173(C), pages 225-237.
    5. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.
    6. Miller, Clayton & Nagy, Zoltán & Schlueter, Arno, 2018. "A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1365-1377.
    7. 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.
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    Cited by:

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    3. Andrews, Abigail & Jain, Rishee K., 2022. "Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking," Applied Energy, Elsevier, vol. 327(C).
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    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. Claudio Caromba & Corné Schutte & Jean van Laar, 2023. "Application of Clustering Techniques for Improved Energy Benchmarking on Deep-Level Mines," Energies, MDPI, vol. 16(19), pages 1-18, September.
    7. Zhiang Zhang & Ali Cheshmehzangi & Saeid Pourroostaei Ardakani, 2021. "A Data-Driven Clustering Analysis for the Impact of COVID-19 on the Electricity Consumption Pattern of Zhejiang Province, China," Energies, MDPI, vol. 14(23), pages 1-22, December.
    8. Tepe, Benedikt & Haberschusz, David & Figgener, Jan & Hesse, Holger & Uwe Sauer, Dirk & Jossen, Andreas, 2023. "Feature-conserving gradual anonymization of load profiles and the impact on battery storage systems," Applied Energy, Elsevier, vol. 343(C).
    9. Wang, Qiaochu & Ding, Yan & Kong, Xiangfei & Tian, Zhe & Xu, Linrui & He, Qing, 2022. "Load pattern recognition based optimization method for energy flexibility in office buildings," Energy, Elsevier, vol. 254(PC).
    10. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
    11. Westermann, Paul & Deb, Chirag & Schlueter, Arno & Evins, Ralph, 2020. "Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data," Applied Energy, Elsevier, vol. 264(C).

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