A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Lizhen Wu,Xiaohong Hao,Chun Kong,Wei Chen

doi:10.1155/2020/1428104

Lizhen Wu, Xiaohong Hao + Show 2 more

Open Access

https://doi.org/10.1155/2020/1428104

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Abstract

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.

Highlights

Due to the difficulty of large-scale storage of electrical energy and electrical energy changes in power demand, it is required that the system power generation should be dynamically balanced with changes in load [1, 2]
Many load forecasting methods based on massive data were emerged, and these methods are mainly divided into two categories, one is traditional statistical methods [8]: they are most frequently used in the early literature, including linear regression (LR) analysis approach and autoregressive moving average (ARMA) approach [9]
To verify the superiority of the gated recurrent unit (GRU)-convolutional neural networks (CNN) model in short-term load forecasting, the proposed method was compared with back-propagation neural network (BPNN), GRU, and CNN models in a real-world experiment. e four models were trained and tested, and mean absolute percentage error (MAPE) and root mean square error (RMSE) were used as the evaluation indexes. e results of the experiments demonstrate that the GRU-CNN model achieves the best predicting performance in Short-term load forecasting (STLF) among the four models

Summary

Introduction

Due to the difficulty of large-scale storage of electrical energy and electrical energy changes in power demand, it is required that the system power generation should be dynamically balanced with changes in load [1, 2]. A combined model, which used the back-propagation neural network (BPNN) with the multilabel algorithm based on K-nearest neighbor (K-NN) and K-means, was proposed for STLF in [18]; BPNN is a feedforward neural network, and it cannot well learn time sequence data in the power system [19]. In order to make full use of the various data in the power system and achieve accurate STLF, the GRU-CNN hybrid neural network model was proposed, which combines the GRU model with the CNN model. To verify the superiority of the GRU-CNN model in short-term load forecasting, the proposed method was compared with BPNN, GRU, and CNN models in a real-world experiment.

The GRU-CNN Hybrid Neural Network Model

Experiments and Results

Conclusions