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Recent advances in the analysis of residential electricity consumption and applications of smart meter data

Author

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  • Yildiz, B.
  • Bilbao, J.I.
  • Dore, J.
  • Sproul, A.B.

Abstract

The emergence of smart grid technologies and applications has meant there is increasing interest in utilising smart meters. Smart meter penetration has significantly increased over the last decade and they are becoming more widespread globally. Companies such as Google, Nest, Intel, General Electric and Amazon are amongst those companies which have been developing end use applications such as home and battery energy management systems which leverage smart meter data. In addition, utilities and networks are becoming more aware of the potential benefits of using household smart meter data in demand side management strategies such as energy efficiency and demand response. Motivated by this fact, the amount of research in this area has grown considerably in recent years. This paper reviews the most recent methods and techniques for using smart meter data such as forecasting, clustering, classification and optimization. The study covers various applications such as Home and Battery Energy Management Systems and demand response strategies enabled by the analysis of smart meter data. From a comprehensive review of the literature, it was observed that there are remarkable discrepancies between the studies, which make in-depth comparison and analysis challenging. Data analysis and reporting guidelines are suggested for studies which use smart meter data. These guidelines could provide a consistent and common framework which could enhance future research.

Suggested Citation

  • Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    • Handle: RePEc:eee:appene:v:208:y:2017:i:c:p:402-427
    DOI: 10.1016/j.apenergy.2017.10.014
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    Cited by:

    1. Islam, Md. Zahidul & Lin, Yuzhang & Vokkarane, Vinod M. & Yu, Nanpeng, 2023. "Robust learning-based real-time load estimation using sparsely deployed smart meters with high reporting rates," Applied Energy, Elsevier, vol. 352(C).
    2. Yang, Kaixiang & Chen, Wuxing & Bi, Jichao & Wang, Mengzhi & Luo, Fengji, 2023. "Multi-view broad learning system for electricity theft detection," Applied Energy, Elsevier, vol. 352(C).

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