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Cross-domain feature selection and coding for household energy behavior

Author

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  • Tong, Xing
  • Li, Ran
  • Li, Furong
  • Kang, Chongqing

Abstract

Household energy behavior is a key factor that dictates energy consumption, efficiency and conservation. In the past, household energy behavior was typically unknown because conventional meters only recorded the total amount of energy consumed for a household over a significant period of time. The rollout of smart meters enabled real-time household energy consumption to be recorded and analyzed. This paper uses smart meter readings from more than 5000 Irish households to identify energy behavior indicators through a cross-domain feature selection and coding approach. The idea is to extract and connect customers' features from energy domain and demography domain, i.e., smart metering data and household information. Smart metering data are characterized by typical energy spectral patterns, whereas household information is encoded as the energy behavior indicator. The results show that employment status and internet usage are highly correlated with household energy behavior in Ireland because employment status and internet usage have an important effect on lifestyle, including when to work, play, and rest, and hence yield a difference in electricity use style. The proposed approach offers a simple, transparent and effective alternative to a challenging cross-domain matching problem with massive smart metering data and energy behavior indicators.

Suggested Citation

  • Tong, Xing & Li, Ran & Li, Furong & Kang, Chongqing, 2016. "Cross-domain feature selection and coding for household energy behavior," Energy, Elsevier, vol. 107(C), pages 9-16.
    • Handle: RePEc:eee:energy:v:107:y:2016:i:c:p:9-16
    DOI: 10.1016/j.energy.2016.03.135
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    Citations

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    Cited by:

    1. Jacqueline Nicole Adams & Zsófia Deme Bélafi & Miklós Horváth & János Balázs Kocsis & Tamás Csoknyai, 2021. "How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review," Energies, MDPI, vol. 14(9), pages 1-23, April.
    2. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
    3. Alexander Martin Tureczek & Per Sieverts Nielsen, 2017. "Structured Literature Review of Electricity Consumption Classification Using Smart Meter Data," Energies, MDPI, vol. 10(5), pages 1-19, April.
    4. Xu, Xiaojing & Chen, Chien-fei & Zhu, Xiaojuan & Hu, Qinran, 2018. "Promoting acceptance of direct load control programs in the United States: Financial incentive versus control option," Energy, Elsevier, vol. 147(C), pages 1278-1287.
    5. Luan, Bingjiang & Zou, Hong & Huang, Junbing, 2023. "Digital divide and household energy poverty in China," Energy Economics, Elsevier, vol. 119(C).
    6. Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
    7. Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.
    8. 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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