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Deep learning model for short-term power load prediction based on Bootstrap error correction
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Yuchen ZHANG, Xuesong JIANG, Chunwei LI, Sen LIU
Thermal Power Generation | 2023, 52(3) : 121 - 129
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Thermal Power Generation | 2023, 52(3): 121-129
• Operation control and optimal scheduling technologies of wind power system •
Deep learning model for short-term power load prediction based on Bootstrap error correction
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Yuchen ZHANG, Xuesong JIANG, Chunwei LI, Sen LIU
Affiliations
  • College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China
Published: 2023-03-25 doi: 10.19666/j.rlfd.202211272
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Aiming at the problem of weak internal regularity caused by the characteristics of nonlinear and strong fluctuation of load data, a TCN-WOA-BiLSTM-Attention power load short-term prediction model based on Bootstrap error correction was constructed. Temporal convolutional network (TCN) was used to extract temporal features and the contribution of important information to the features was highlighted through the Attention mechanism. The whale optimization algorithm (WOA) was employed to find the optimal bidirectional long short term memory network (BiLSTM) hyperparameters, thus to reduce the negative impact of manual search hyperparameters and then forecast. Based on Bootstrap analysis on error distribution of the prediction interval, the necessity of correcting the prediction result was judged by whether the PICP was lower than the corresponding confidence, and the reasonable correction range was selected. The results show that, the error correction based on the Bootstrap method can avoid the problem of insufficient correction and excessive correction. Compared with the method of correcting the whole error sequence, it is more scientific and improves the prediction accuracy of the model to the greatest extent.

short-term power load prediction  /  Bootstrap  /  error correction  /  temporal convolutional neural network  /  whale optimization algorithm
Yuchen ZHANG, Xuesong JIANG, Chunwei LI, Sen LIU. Deep learning model for short-term power load prediction based on Bootstrap error correction[J]. Thermal Power Generation, 2023 , 52 (3) : 121 -129 . DOI: 10.19666/j.rlfd.202211272
Funding
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  • Natural Science Foundation of Heilongjiang Province(LH2019E001)
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Year 2023 volume 52 Issue 3
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77
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Article Info
doi: 10.19666/j.rlfd.202211272
  • Receive Date：2022-11-10
  • Online Date：2026-01-23
  • Published：2023-03-25
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  • Received：2022-11-10
Funding
Natural Science Foundation of Heilongjiang Province(LH2019E001)
Affiliations
    College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202211272
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