| 王立则,谢东,周立峰,王汉青.基于Attention机制和ResNet的CNN-BiLSTM短期电力负荷预测模型研究[J].南华大学学报(自然科学版),2023,(1):33~39, 86.[WANG Lize,XIE Dong,ZHOU Lifeng,WANG Hanqing.Research on CNN-BiLSTM Short-term Power Load Forecasting Model Based on Attention Mechanism and ResNet[J].Journal of University of South China(Science and Technology),2023,(1):33~39, 86.] |
| 基于Attention机制和ResNet的CNN-BiLSTM短期电力负荷预测模型研究 |
| Research on CNN-BiLSTM Short-term Power Load Forecasting Model Based on Attention Mechanism and ResNet |
| 投稿时间：2022-10-03 |
| DOI：10.19431/j.cnki. 1673-0062.2023.01.005 |
| 中文关键词: 短期负荷预测 卷积神经网络 双向长短期记忆 注意力机制 残差网络 |
| 英文关键词:short-term load forecasting convolutional neural network bi-directional long short-term memory attention mechanism residual network |
| 基金项目:国家自然科学基金资助项目(U1867221);湖南省教育厅科学研究项目(19C1568) |
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| 中文摘要: |
| 短期电力负荷预测有利于电力系统的高效运行,对电力市场实现有效调度有重要意义。短期电力负荷受多种因素影响,波动性大、随机性强,使得其预测准确率低。双向长短期记忆网络和卷积神经网络难以在短期负荷序列中提取足够多的信息,本文提出了一种结合注意力机制和残差网络的卷积神经网络-双向长短期记忆网络短期负荷预测方法。首先利用基准模型卷积神经网络-双向长短期记忆网络对输入特征进行信息提取,然后利用注意力机制突出提取到的关键信息,最后通过残差网络创建残差层以充分学习时序特征。通过某公开数据集进行实验,结果表明该方法的平均绝对百分比误差达到2.80%,均方根误差达到2.15,并与常用的五种模型预测结果对比,验证了所提模型的准确性及有效性。 |
| 英文摘要: |
| Short term power load forecasting is beneficial to the efficient operation of power system and is of great significance to the effective dispatching of power market. Short term power load is affected by many factors, with large volatility and strong randomness, which makes its prediction accuracy low. It is difficult for BiLSTM and CNN to extract enough information from short-term load series. This paper proposes a CNN BiLSTM short-term load forecasting method combining Attention and ResNet. First, use the benchmark model BiLSTM and CNN to extract information from the input features, and use the Attention mechanism to highlight the extracted key information. Finally, ResNet creates a residual layer to fully learn the temporal features. Experiments on an open dataset show that the of this method reaches 2.80%, and the reaches 2.15. Compared with the prediction results of five commonly used models, the accuracy and effectiveness of the proposed model are verified. |
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