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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model
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A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

机译：基于GRU-CNN混合神经网络模型的短期负荷预测方法

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摘要

Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.
机译：短期负荷预测（STLF）在提高电力系统操作的经济和稳定性方面发挥着非常重要的作用。随着智能电表和智能传感器广泛部署在电力系统中，产生了大量数据但未充分利用，这些数据是复杂和多样化的，而且大多数STLF方法都无法处理这种巨大，复杂和多样化数据。为了更好的STLF精度，提出了一种GRU-CNN混合神经网络模型，其结合了所门控复发单元（GRU）和卷积神经网络（CNN）;时间序列数据的特征向量由GRU模块提取，并且由CNN模块提取其他高维数据的特征向量。在真实的实验中测试了所提出的模型，并且GRU-CNN模型的平均绝对百分比误差（MAPE）和均方根误差（RMSE）是BPNN，GRU和CNN预测方法中最低的。所提出的GRU-CNN模型可以更充分地使用数据并实现更准确的短期负荷预测。

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