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ShuffleTransformerMulti-headAttentionNet network for user load forecasting

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  • Yin, Linfei
  • Ju, Linyi

Abstract

With the development of intelligent power load forecasting, optimizing and improving algorithms to increase the accuracy and speed of load forecasting is a key part of responding to policy development opportunities. However, since the load data is affected by many factors, the output is characterized by volatility, nonlinearity and non-smoothness, and the volume is large, and it is difficult to mine the hidden information, the stable operation and economic benefits of the power system will become a challenge. To improve data prediction accuracy and maintain power system stable operation, this study proposes a ShuffleTransformerMulti-headAttentionNet network model. The structural feature of the network model is that two CNN networks are input to a Transformer model in a parallel way. The ShuffleTransformerMulti-headAttentionNet network model is compared with 20 network models such as Shufflenet and GooleNet. To better improve the applicability of the model, we discuss small-scale, high temporal resolution and large-scale, low temporal resolution historical power load data. The feasibility and reliability of the ShuffleTransformerMulti-headAttentionNet algorithm is verified by simulating the input data of Case 1:292 × 96 and Case 2:31720 × 24, respectively. The experimental results show that the precision of the ShuffleTransformerMulti-headAttentionNet network is the highest in the comparison experiment, with root mean squared error, mean squared error, mean absolute error and symmetric mean absolute percentage error as the reference standards. In case 1, RMSE, MSE, MAE, MAPE, and SMAPE of ShuffleTransformerMulti-headAttentionNet network are optimized 20.27 %–100 %, 36.43 %–100 %, 11.51 %–100 %, 0.76 %–100 %, 5.97 %–96.00 % compared with other algorithms, respectively. In case 2, RMSE, MSE, MAE, MAPE, and SMAPE of ShuffleTransformerMulti-headAttentionNet network are optimized by 21.65 %–100 %, 38.62 %–100 %, 36.20 %–100 %, 12.72 %–100 %, 26.00 %–96.05 % compared with other algorithms, respectively.

Suggested Citation

  • Yin, Linfei & Ju, Linyi, 2025. "ShuffleTransformerMulti-headAttentionNet network for user load forecasting," Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s036054422501179x
    DOI: 10.1016/j.energy.2025.135537
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