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Data quality of electricity consumption data in a smart grid environment

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  • Chen, Wen
  • Zhou, Kaile
  • Yang, Shanlin
  • Wu, Cheng

Abstract

With the increasing penetration of traditional and emerging information technologies in the electric power industry, together with the rapid development of electricity market reform, the electric power industry has accumulated a large amount of data. Data quality issues have become increasingly prominent, which affect the accuracy and effectiveness of electricity data mining and energy big data analytics. It is also closely related to the safety and reliability of the power system operation and management based on data-driven decision support. In this paper, we study the data quality of electricity consumption data in a smart grid environment. First, we analyze the significance of data quality. Also, the definition and classification of data quality issues are explained. Then we analyze the data quality of electricity consumption data and introduce the characteristics of electricity consumption data in a smart grid environment. The data quality issues of electricity consumption data are divided into three types, namely noise data, incomplete data and outlier data. We make a detailed discussion on these three types of data quality issues. In view of that outlier data is one of the most prominent issues in electricity consumption data, so we mainly focus on the outlier detection of electricity consumption data. This paper introduces the causes of electricity consumption outlier data and illustrates the significance of the electricity consumption outlier data from the negative and positive aspects respectively. Finally, the focus of this paper is to provide a review on the detection methods of electricity consumption outlier data. The methods are mainly divided into two categories, namely the data mining-based and the state estimation-based methods.

Suggested Citation

  • Chen, Wen & Zhou, Kaile & Yang, Shanlin & Wu, Cheng, 2017. "Data quality of electricity consumption data in a smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 98-105.
    • Handle: RePEc:eee:rensus:v:75:y:2017:i:c:p:98-105
    DOI: 10.1016/j.rser.2016.10.054
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    References listed on IDEAS

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    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. Zhou, Kaile & Yang, Shanlin & Shen, Chao & Ding, Shuai & Sun, Chaoping, 2015. "Energy conservation and emission reduction of China’s electric power industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 10-19.
    3. Zhou, Kaile & Yang, Shanlin, 2016. "Understanding household energy consumption behavior: The contribution of energy big data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 810-819.
    4. Zhou, Kaile & Yang, Shanlin & Chen, Zhiqiang & Ding, Shuai, 2014. "Optimal load distribution model of microgrid in the smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 304-310.
    5. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    6. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    7. Zhou, Kaile & Yang, Shanlin, 2015. "Demand side management in China: The context of China’s power industry reform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 954-965.
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    Cited by:

    1. Noussan, Michel, 2018. "Performance based approach for electricity generation in smart grids," Applied Energy, Elsevier, vol. 220(C), pages 231-241.
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    3. Viktorija Bobinaite & Marialaura Di Somma & Giorgio Graditi & Irina Oleinikova, 2019. "The Regulatory Framework for Market Transparency in Future Power Systems under the Web-of-Cells Concept," Energies, MDPI, vol. 12(5), pages 1-26, March.
    4. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    5. Jing Li & Meng Guo & Kevin Lo, 2019. "Estimating Housing Vacancy Rates in Rural China Using Power Consumption Data," Sustainability, MDPI, vol. 11(20), pages 1-13, October.
    6. Ahmad, Tanveer & Chen, Huanxin & Wang, Jiangyu & Guo, Yabin, 2018. "Review of various modeling techniques for the detection of electricity theft in smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2916-2933.
    7. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    8. Minkyung Kim & Sangdon Park & Joohyung Lee & Yongjae Joo & Jun Kyun Choi, 2017. "Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data," Energies, MDPI, vol. 10(10), pages 1-20, October.
    9. Marzal, Silvia & Salas, Robert & González-Medina, Raúl & Garcerá, Gabriel & Figueres, Emilio, 2018. "Current challenges and future trends in the field of communication architectures for microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3610-3622.
    10. Choi, Jongwoo & Lee, Il-Woo & Cha, Suk-Won, 2022. "Analysis of data errors in the solar photovoltaic monitoring system database: An overview of nationwide power plants in Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    11. Liu, Liqi & Liu, Yanli, 2022. "Load image inpainting: An improved U-Net based load missing data recovery method," Applied Energy, Elsevier, vol. 327(C).

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