基于TCN-GRU模型的短期负荷预测方法
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TM715

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国家自然科学基金资助项目(51877044)


A forecasting method for short-term load based on TCN-GRU model
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The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    为了进一步提高短期负荷的预测精度,为电力系统的稳定运行提供更加有力的保障,文中提出了一种将时间卷积网络(TCN)和门限循环单元(GRU)相结合的短期负荷预测方法TCN-GRU。首先,将采集的训练数据划分为时序数据和非时序数据;其次,将时序数据输入到TCN模型中以提取时序特征;然后,将提取出来的时序特征与非时序数据组合起来输入到GRU模型中对模型进行训练;最后,利用训练好的模型实现对短期电力负荷的预测。基于广东省佛山市某行业真实负荷数据验证了TCN-GRU模型的负荷预测能力,并通过对比多种深度学习模型的预测效果,验证该模型具有更高精度的短期负荷预测能力。

    Abstract:

    In order to improve the accuracy of short-term load prediction and provide a more powerful guarantee for stable operation of power system, a short-term load prediction method that applies temporal convolutional network (TCN) and gated recurrent unit (GRU) is proposed in this paper. Firstly, training data are divided into two types, namely time series data and non-time series data. Secondly, time series data are selected as the input of TCN model to extract time series features. Then, time series features and not-time series date are input in the GRU model for training. Finally, the trained model is used to predict short-term power load. Based on real load data of an industry in Foshan City, Guangdong Province, the load forecasting ability of TCN-GRU model is verified. By comparing with the prediction effects of other four deep learning models, the proposed model in this paper is verified to have the ability of forecasting much more accurately for short-term load.

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郭玲,徐青山,郑乐.基于TCN-GRU模型的短期负荷预测方法[J].电力工程技术,2021,40(3):66-71

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  • 收稿日期:2020-11-20
  • 最后修改日期:2020-12-21
  • 录用日期:2020-07-19
  • 在线发布日期: 2021-06-11
  • 出版日期: 2021-05-28
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