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Deep ResNet-Based Ensemble Model for Short-Term Load Forecasting in Protection System of Smart Grid

Author

Listed:
  • Wenhao Chen

    (School of Transportation, Fujian University of Technology, Fuzhou 350118, China)

  • Guangjie Han

    (School of Transportation, Fujian University of Technology, Fuzhou 350118, China
    Department of Information and Communication System, Hohai University, Changzhou 213022, China)

  • Hongbo Zhu

    (School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China)

  • Lyuchao Liao

    (School of Transportation, Fujian University of Technology, Fuzhou 350118, China)

  • Wenqing Zhao

    (School of Transportation, Fujian University of Technology, Fuzhou 350118, China)

Abstract

Short-term load forecasting is a key digital technology to support urban sustainable development. It can further contribute to the efficient management of the power system. Due to strong volatility of the electricity load in the different stages, the existing models cannot efficiently extract the vital features capturing the change trend of the load series. The above problem limits the forecasting performance and creates the challenge for the sustainability of urban development. As a result, this paper designs the novel ResNet-based model to forecast the loads of the next 24 h. Specifically, the proposed method is composed of a feature extraction module, a base network, a residual network, and an ensemble structure. We first extract the multi-scale features from raw data to feed them into the single snapshot model, which is modeled with a base network and a residual network. The networks are concatenated to obtain preliminary and snapshot labels for each input, successively. Also, the residual blocks avoid the probable gradient disappearance and over-fitting with the network deepening. We introduce ensemble thinking for selectively concatenating the snapshots to improve model generalization. Our experiment demonstrates that the proposed model outperforms exiting ones, and the maximum performance improvement is up to 4.9% in MAPE.

Suggested Citation

  • Wenhao Chen & Guangjie Han & Hongbo Zhu & Lyuchao Liao & Wenqing Zhao, 2022. "Deep ResNet-Based Ensemble Model for Short-Term Load Forecasting in Protection System of Smart Grid," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16894-:d:1005526
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    References listed on IDEAS

    as
    1. Singh, Priyanka & Dwivedi, Pragya, 2018. "Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem," Applied Energy, Elsevier, vol. 217(C), pages 537-549.
    2. Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
    3. Singh, Priyanka & Dwivedi, Pragya, 2019. "A novel hybrid model based on neural network and multi-objective optimization for effective load forecast," Energy, Elsevier, vol. 182(C), pages 606-622.
    4. Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
    5. Deihimi, Ali & Showkati, Hemen, 2012. "Application of echo state networks in short-term electric load forecasting," Energy, Elsevier, vol. 39(1), pages 327-340.
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