| 结合改进残差网络和Bi-LSTM的短期电力负荷预测 |
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| 引用本文： | 李艳波,尹镨,陈俊硕,张钰,姚博彬,刘维宇.结合改进残差网络和Bi-LSTM的短期电力负荷预测[J].哈尔滨工业大学学报,2023,55(8):79-86. |
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| 作者姓名： | 李艳波 尹镨 陈俊硕 张钰 姚博彬 刘维宇 |
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| 作者单位： | 长安大学 能源与电气工程学院,西安 710064 |
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| 基金项目： | 国家重点研发计划(2021YFB1600202) |
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| 摘 要： | 为充分挖掘电力负荷历史数据的潜在特征,提高短期负荷预测模型的预测精度,提出了一种由改进残差网络（ResNetPlus）、注意力机制（Attention mechanism,AM）和双向长短期记忆网络（Bi-directional long short-term memory,Bi-LSTM）结合而成的残差AM-Bi-LSTM预测模型。该模型将历史负荷、温度和所预测日期的特征作为输入,在Bi-LSTM模型基础上,引入多层改进残差网络提取输入数据的隐藏特征,有效克服了网络隐藏层数加深导致的网络退化问题,使模型的反向传播能力大幅提升；加入注意力机制,分析网络中输入信息与当前负荷的相关性并突出重要信息的影响,从而提高模型的速度与准确率；使用Snapshot策略集成收敛于不同局部极小值的多个模型,以提升模型的准确率和鲁棒性。最后,使用美国ISO-NE数据集进行模拟预测,测试结果表明:所提模型的平均预测精度达到98.27%；在连续的12个月中采用该模型的平均预测精度相比于LSTM模型提高了2.87%；在不同季节下采用该模型的平均预测精度相比于AM-Bi-LSTM和ResNetPlus模型分别提高了1.05%和1.16%。说明所提模型相较于对比模型具有较高的准确率、鲁棒性以及泛化能力。
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| 关 键 词： | 深度学习 短期负荷预测 长短期记忆网络 注意力机制 残差网络 |
| 收稿时间： | 2022/8/29 0:00:00 |
Short-term power load forecasting based on combination of residual network and Bi-LSTM |
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| LI Yanbo,YIN Pu,CHEN Junshuo,ZHANG Yu,YAO Bobin,LIU Weiyu.Short-term power load forecasting based on combination of residual network and Bi-LSTM[J].Journal of Harbin Institute of Technology,2023,55(8):79-86. |
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| Authors: | LI Yanbo YIN Pu CHEN Junshuo ZHANG Yu YAO Bobin LIU Weiyu |
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| Abstract: | To fully develop the potential characteristics of electric load historical data and improve the prediction accuracy of the short-term load forecasting model, a residual AM-Bi-LSTM prediction model combining improved residual network (ResNetPlus), attention mechanism (AM) and bi-directional long short-term memory network(Bi-LSTM) is proposed in this paper. This model takes historical load, temperature and the features of the predicted date as input. First, based on the Bi-LSTM prediction model, multi-layer improved residual networks are introduced to extract the hidden features of input data, which solves the problem of network degradation caused by the deepening of hidden layers of neural networks, and greatly improves the back propagation ability of the model. Second, the attention mechanism is used to analyze the correlation between input information and current load in the network and highlight the impact of important information, thus improving the speed and accuracy of the model. Third, the Snapshot strategy is used to integrate multiple models that converge on different local minima, in order to improve the accuracy and robustness of the model. Finally, the US ISO-NE Dataset is used to verify the performance of the model. The experimental results show that the proposed model has achieved an average prediction accuracy of 98.27%. The average prediction accuracy over 12 consecutive months with the proposed model has improved by 2.87% compared to the traditional LSTM model. In addition, the average prediction accuracy under different seasons based on the proposed model has improved by 1.03% and 1.16% compared to the AM-Bi-LSTM and ResNetPlus models, respectively. This indicates that compared to the contrast model, the residual AM-Bi-LSTM model has higher accuracy, robustness and generalization ability. |
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| Keywords: | deep learning short term load forecasting long short-term memory(LSTM) attention mechanism residual network |
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