Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model

doi:10.11930/j.issn.1004-9649.202306094

Abstract

Abstract:

To improve the prediction accuracy of multi-region power load, an ultra-short-term multi-region power load forecasting model based on Spearman-GCN-GRU is proposed with focus on the spatial-temporal correlation analysis of multi-region power data. Firstly, the Spearman correlation coefficient is used to analyze the spatial-temporal correlation of power load in different regions and construct the Spearman adjacency matrix. And then, the graph convolutional network (GCN) and gated recurrent unit (GRU) are used to respectively extract the spatial and temporal features from the data. Finally, the multilayer perceptron (MLP) is used to decode and output the prediction results. Through comparison with the distance adjacency matrix-based models, the Spearman-GCN-GRU model is proved to be feasible. In terms of prediction accuracy, the Spearman-GCN-GRU model are optimal in common evaluation indexes compared with traditional statistical models and neural network models. Specifically, in terms of the root mean square error (RMSE), the Spearman-GCN-GRU model exhibits a respective decrease of 13.90%, 11.66%, and 8.36% compared to the GRU, GCN and deep neural network (DNN) models, demonstrating its superior predictive performance.

Key words: multi-region power load prediction, spatial-temporal correlation analysis of power data, Spearman correlation coefficient, graph convolutional network, gated recurrent unit

Junying WU, Xin LU, Hong LIU, Bin ZHANG, Shouliang CHAI, Yunchun LIU, Jianan WANG. Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model[J]. Electric Power, 2024, 57(6): 131-140.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202306094

https://www.electricpower.com.cn/EN/Y2024/V57/I6/131

Figures/Tables 14

Fig.1 Geographic correlation and spatial-temporal correlation

Fig.2 Geographic adjacency matrix and spatial-temporal adjacency matrix

Fig.3 The regional spatial-temporal correlation graph constructed based on Spearman correlation coefficient

Fig.4 GCN convolution process

Fig.5 Spearman-GCN-GRU-based ultra-short-term multi-region power load forecasting model

Fig.6 Spearman correlation coefficient among 10 regions

Fig.7 Adjacency matrices with different thresholds

Table 1 Comparison of prediction accuracy with threshold varying from 0.80 to 0.90

阈值	区域数	RMSE/W	RRMSE/%	MAE/W	MAPE/%	PA/%
0.80	10	8.7759	13.7826	5.4075	8.5570	91.4119
0.85	10	8.2328	12.7745	4.5394	7.2324	92.9375
0.90	10	7.3495	12.5806	4.6102	7.1035	93.0633

Table 2 Comparison of prediction accuracy between threshold 0.90 and 0.95

阈值	区域数	RMSE/W	RRMSE/%	MAE/W	MAPE/%	PA/%
0.90	6	6.2266	11.7588	4.1887	7.0101	92.8098
0.95	6	6.6723	12.8625	3.8127	6.3530	93.5112

Fig.8 Spearman adjacency matrices and geographic adjacency matrices

Table 3 Comparison of prediction accuracy with different adjacency matrices

方法	区域数	RMSE/W	RRMSE/%	MAE/W	MAPE/%	PA/%
Geographic-GCN-GRU	9	7.4849	12.6664	4.8888	7.9478	92.7178
Spearman-GCN-GRU	9	6.6723	11.8877	4.4881	7.0999	93.0428

Fig.9 Prediction results of regions from 1 to 4 in test dataset

Table 4 Prediction results of different models

预测模型	RMSE/W	RRMSE/%	MAE/W	MAPE/%	PA/%
SVR （kernel='rbf'）	9.4144	13.6433	6.5627	9.9044	90.1812
GRU	8.5369	13.3252	4.6561	8.3685	92.5577
GCN	8.3195	12.5988	5.0038	7.7763	92.1970
DNN	8.0200	14.0683	5.1853	8.0953	91.0630
Spearman- GCN-GRU	7.3495	11.7929	4.6102	7.1035	93.0633

Fig.10 Prediction results of different models in multiple regions

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