Deep learning model for short-term power load prediction based on Bootstrap error correction

ZHANG Yuchen; JIANG Xuesong; LI Chunwei; LIU Sen

doi:10.19666/j.rlfd.202211272

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Deep learning model for short-term power load prediction based on Bootstrap error correction

Thermal Power Generation Vol. 52, Issue 3, Pages: 121-129(2023)

更新时间：2025-09-28

- Deep learning model for short-term power load prediction based on Bootstrap error correction
- 作者机构：
  
  东北林业大学工程技术学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202211272
  CLC： TP18;TM715
- Published：2023
- 稿件说明：
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ZHANG Yuchen, JIANG Xuesong, LI Chunwei, et al. Deep learning model for short-term power load prediction based on Bootstrap error correction[J]. Thermal Power Generation, 2023, 52(3): 121-129.
DOI：

ZHANG Yuchen, JIANG Xuesong, LI Chunwei, et al. 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.

摘要

针对负荷数据非线性、强波动性等特点导致数据规律性较弱电力负荷预测模型不准确的问题，构建基于Bootstrap误差修正的TCN-WOA-Bi LSTM-Attention电力负荷短期预测模型。使用时序卷积神经网络（TCN）提取时序特征并通过注意力机制（Attention机制）对特征突出重要信息贡献度，通过鲸鱼优化算法（WOA）寻找双向长短时记忆（Bi LSTM）神经网络最优超参数以减少人工搜索超参数的负面影响后进行预测；基于Bootstrap分析预测区间误差分布，通过覆盖率（PICP）是否低于对应置信度判断对预测结果进行修正的必要性，并选取合理修正范围。仿真结果表明，基于Bootstrap方法进行误差修正避免了修正不足及修正过度的问题，对比将误差序列全部修正的方法更具有科学性，能最大程度提高模型预测精度。

Abstract

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.

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