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Short-Term Load Forecasting Algorithm Based on LST-TCN in Power Distribution Network

Author

Listed:
  • Wanxing Sheng

    (State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Keyan Liu

    (State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Dongli Jia

    (State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Shuo Chen

    (State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Rongheng Lin

    (State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China)

Abstract

In this paper, a neural network model called Long Short-Term Temporal Convolutional Network (LST-TCN) model is proposed for short-term load forecasting. This model refers to the 1-D fully convolution network, causal convolution, and void convolution structure. In the convolution layer, a residual connection layer is added. Additionally, the model makes use of two networks to extract features from long-term data and periodic short-term data, respectively, and fuses the two features to calculate the final predicted value. Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) are used as comparison algorithms to train and forecast 3 h, 6 h, 12 h, 24 h, and 48 h ahead of daily electricity load together with LST-TCN. Three different performance metrics, including pinball loss, root mean squared error (RMSE), and mean absolute error (RASE), were used to evaluate the performance of the proposed algorithms. The results of the test set proved that LST-TCN has better generalization effects and smaller prediction errors. The algorithm has a pinball loss of 1.2453 for 3 h ahead forecast and a pinball loss of 1.4885 for 48 h ahead forecast. Generally speaking, LST-TCN has better performance than LSTM, TCN, and other algorithms.

Suggested Citation

  • Wanxing Sheng & Keyan Liu & Dongli Jia & Shuo Chen & Rongheng Lin, 2022. "Short-Term Load Forecasting Algorithm Based on LST-TCN in Power Distribution Network," Energies, MDPI, vol. 15(15), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5584-:d:877733
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    References listed on IDEAS

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    3. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
    4. Fang, Tingting & Lahdelma, Risto, 2016. "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system," Applied Energy, Elsevier, vol. 179(C), pages 544-552.
    Full references (including those not matched with items on IDEAS)

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