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Characterizing patterns and variability of building electric load profiles in time and frequency domains

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  • Li, Han
  • Wang, Zhe
  • Hong, Tianzhen
  • Parker, Andrew
  • Neukomm, Monica

Abstract

The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

Suggested Citation

  • Li, Han & Wang, Zhe & Hong, Tianzhen & Parker, Andrew & Neukomm, Monica, 2021. "Characterizing patterns and variability of building electric load profiles in time and frequency domains," Applied Energy, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:appene:v:291:y:2021:i:c:s0306261921002397
    DOI: 10.1016/j.apenergy.2021.116721
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    References listed on IDEAS

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    1. D’Oca, Simona & Hong, Tianzhen & Langevin, Jared, 2018. "The human dimensions of energy use in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 731-742.
    2. 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.
    3. 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).
    4. 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:

    1. 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).
    2. Pylorof, Dimitrios & Garcia, Humberto E., 2024. "Situational awareness-enhancing community-level load mapping with opportunistic machine learning," Applied Energy, Elsevier, vol. 366(C).
    3. 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).
    4. Pullinger, Martin & Zapata-Webborn, Ellen & Kilgour, Jonathan & Elam, Simon & Few, Jessica & Goddard, Nigel & Hanmer, Clare & McKenna, Eoghan & Oreszczyn, Tadj & Webb, Lynda, 2024. "Capturing variation in daily energy demand profiles over time with cluster analysis in British homes (September 2019 – August 2022)," Applied Energy, Elsevier, vol. 360(C).

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