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Short-term multiple power type prediction based on deep learning

Author

Listed:
  • Ran Wei

    (Tianjin Polytechnic University)

  • Qirui Gan

    (Tianjin Polytechnic University)

  • Huiquan Wang

    (Tianjin Polytechnic University)

  • Yue You

    (State Grid Electronic Commerce Company, LTD)

  • Xin Dang

    (Tianjin Polytechnic University)

Abstract

This paper proposes a method based on a 4-layer deep neural network model by stacked denoising auto-encoders to analyze four types of power data: current (I), voltage (U), active power (P) and reactive power (Q). We collect 7 days of household power data. In the beginning, the prediction accuracy rate can reach 82.45% when 1-h historical data are used to predict the data for the following 5 min. In order to optimize the parameters of this model, data over a 3-month period are collected. The prediction accuracy rate is 95.52% when three-day historical data are used to predict the data for the next hour. Finally, supplemental experiments are added to verify that the current change has a greater impact on the model. The 3-month data set is used as the training set. Extract 2 weeks of data from 3 months of data, and the 2-week data is divided into two test sets. The effect of the model on the prediction accuracy from 7:00 in the morning to 24:00 in the evening, and from 0:00 in the evening to 7:00 in the morning is studied. The accuracy rates are 95.05% and 99.02%, respectively. It shows that the prediction accuracy of the model is higher for the period with a lower frequency of power consumption than the period with a higher frequency of power consumption, and that the change of the current has a greater impact on the prediction of the model. Finally, we prove that the effect of the 4-layer network is better than that of the 3-layer, 5-layer and 7-layer network models.

Suggested Citation

  • Ran Wei & Qirui Gan & Huiquan Wang & Yue You & Xin Dang, 2020. "Short-term multiple power type prediction based on deep learning," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(4), pages 835-841, August.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:4:d:10.1007_s13198-019-00885-8
    DOI: 10.1007/s13198-019-00885-8
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    References listed on IDEAS

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    1. Mohammadi, Kasra & Shamshirband, Shahaboddin & Yee, Por Lip & Petković, Dalibor & Zamani, Mazdak & Ch, Sudheer, 2015. "Predicting the wind power density based upon extreme learning machine," Energy, Elsevier, vol. 86(C), pages 232-239.
    2. Frank Emmert-Streib & Matthias Dehmer, 2007. "Nonlinear Time Series Prediction Based On A Power-Law Noise Model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(12), pages 1839-1852.
    3. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods," Energy, Elsevier, vol. 36(7), pages 3968-3978.
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    Cited by:

    1. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    2. Lenin Kanagasabai, 2022. "Real power loss reduction by quantum based Ptilonorhynchus violaceus optimization and Haliastur Indus algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1913-1931, August.

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