Short-term Power Load Forecasting Based on CNN-BiGRU-Attention

doi:10.11985/2024.01.037

Abstract

Abstract:

In the light of the problems of strong randomness of current power load data, complex influencing factors and low accuracy of traditional single forecasting model, combining the different advantages of convolutional neural network(CNN), bi-directional gated recurrent unit(BiGRU) and attention mechanism(Attention) in short-term load forecasting, a hybrid CNN-BiGRU-Attention based prediction model is proposed. First the initial features of historical load and meteorological data is extracted through CNN, then time series association of feature data is further excavated by BiGRU. Then attention mechanism is introduced to give different weights to the output status of BiGRU, strengthen key features. Finally, load prediction is completed. The experimental results show that the mean absolute percentage error(MAPE), root mean square error(RMSE) and R-square(R²) of the model are 0.167%, 0.057% and 0.993, respectively, and the three indicators are significantly better than other models, with higher prediction accuracy and stability. The advantages of this proposed model in short-term load forecasting are verified.

Key words: Convolutional neural network, bi-directional gated recurrent unit, attention mechanism, short-term power load forecasting, hybrid prediction model

CLC Number:

TP183

REN Shuang, YANG Kai, SHANG Jicai, QI Jiming, WEI Xiangyu, CAI Yonggen. Short-term Power Load Forecasting Based on CNN-BiGRU-Attention[J]. Journal of Electrical Engineering, 2024, 19(1): 344-350.

Figures/Tables 12

References 20

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分类	范围
训练集	2012年1月1日—2013年12月31日
验证集	2014年1月1日—2014年6月30日
测试集	2014年7月1日—2014年12月31日

模型	超参数
模型	网络层数	神经元个数	迭代次数	批量大小	学习率
GRU	2	均为10	100	16	0.01
BiGRU	2	均为10	100	16	0.01
BiGRU-Attention	2	均为10	100	128	0.01
CNN-BiGRU-Attention	CNN：2	CNN1：32	150	128	0.01
	CNN：2	CNN2：64
	BiGRU：2	均为20

模型	MAPE(%)	RMSE(%)	R²
GRU	0.476	0.159	0.949
BiGRU	0.391	0.124	0.969
BiGRU-Attention	0.364	0.091	0.983
CNN-BiGRU-Attention	0.167	0.057	0.993