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Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders

Author

Listed:
  • Xiaoyao Huang

    (State Grid Zhejiang Electric Power Corporation, Hangzhou 310007, China)

  • Tianbin Hu

    (School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China)

  • Chengjin Ye

    (College of electric engineering, Zhejiang University, Hangzhou 310027, China)

  • Guanhua Xu

    (College of electric engineering, Zhejiang University, Hangzhou 310027, China)

  • Xiaojian Wang

    (State Grid Zhejiang Electric Power Corporation, Hangzhou 310007, China)

  • Liangjin Chen

    (State Grid Zhejiang Electric Power Corporation, Hangzhou 310007, China)

Abstract

With the development of advanced metering infrastructure (AMI), electrical data are collected frequently by smart meters. Consequently, the load data volume and length increase dramatically, which aggravates the data storage and transmission burdens in smart grids. On the other hand, for event detection or market-based demand response applications, load service entities (LSEs) want smart meter readings to be classified in specific and meaningful types. Considering these challenges, a stacked auto-encoder (SAE)-based load data mining approach is proposed. First, an innovative framework for smart meter data flow is established. On the user side, the SAEs are utilized to compress load data in a distributed way. Then, centralized classification is adopted at remote data center by softmax classifier. Through the layer-wise feature extracting of SAE, the sparse and lengthy raw data are expressed in compact forms and then classified based on features. A global fine-tuning strategy based on a well-defined labeled subset is embedded to improve the extracted features and the classification accuracy. Case studies in China and Ireland demonstrate that the proposed method is more capable to achieve the minimum of error and satisfactory compression ratios (CR) than benchmark compressors. It also significantly improves the classification accuracy on both appliance and house level datasets.

Suggested Citation

  • Xiaoyao Huang & Tianbin Hu & Chengjin Ye & Guanhua Xu & Xiaojian Wang & Liangjin Chen, 2019. "Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders," Energies, MDPI, vol. 12(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:653-:d:206898
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    References listed on IDEAS

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    1. Ye, Chengjin & Ding, Yi & Song, Yonghua & Lin, Zhenzhi & Wang, Lei, 2018. "A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing," Applied Energy, Elsevier, vol. 232(C), pages 9-25.
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    Cited by:

    1. Antonio E. Saldaña-González & Andreas Sumper & Mònica Aragüés-Peñalba & Miha Smolnikar, 2020. "Advanced Distribution Measurement Technologies and Data Applications for Smart Grids: A Review," Energies, MDPI, vol. 13(14), pages 1-34, July.
    2. Vattanak Sok & Sun-Woo Lee & Sang-Hee Kang & Soon-Ryul Nam, 2022. "Deep Neural Network-Based Removal of a Decaying DC Offset in Less Than One Cycle for Digital Relaying," Energies, MDPI, vol. 15(7), pages 1-14, April.
    3. Krzysztof Lowczowski & Jozef Lorenc & Andrzej Tomczewski & Zbigniew Nadolny & Jozef Zawodniak, 2020. "Monitoring of MV Cable Screens, Cable Joints and Earthing Systems Using Cable Screen Current Measurements," Energies, MDPI, vol. 13(13), pages 1-28, July.
    4. Raneen Younis & Andreas Reinhardt, 2020. "A Study on Fundamental Waveform Shapes in Microscopic Electrical Load Signatures," Energies, MDPI, vol. 13(12), pages 1-19, June.
    5. Mariana Syamsudin & Cheng-I Chen & Sunneng Sandino Berutu & Yeong-Chin Chen, 2024. "Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System," Energies, MDPI, vol. 17(6), pages 1-20, March.
    6. Sun-Bin Kim & Vattanak Sok & Sang-Hee Kang & Nam-Ho Lee & Soon-Ryul Nam, 2019. "A Study on Deep Neural Network-Based DC Offset Removal for Phase Estimation in Power Systems," Energies, MDPI, vol. 12(9), pages 1-19, April.
    7. Rongheng Lin & Shuo Chen & Zheyu He & Budan Wu & Hua Zou & Xin Zhao & Qiushuang Li, 2024. "Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network," Energies, MDPI, vol. 17(16), pages 1-20, August.
    8. Jeng-Wei Lin & Shih-wei Liao & Fang-Yie Leu, 2019. "Sensor Data Compression Using Bounded Error Piecewise Linear Approximation with Resolution Reduction," Energies, MDPI, vol. 12(13), pages 1-20, June.

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