Multi-feature Short-term Prediction of Power Load Components Based on VMD-SE

doi:10.11930/j.issn.1004-9649.202303085

Abstract

Abstract:

To improve the accuracy of power load prediction, a multi-feature short-term prediction method based on VMD-SE for power load components is proposed. Firstly, the variational modal decomposition (VMD) is used to decompose the original load into a series of modal components and residuals, and the decomposition level of VMD is determined by sample entropy (SE). Then the SE values of the original load and modal components are compared, and the original load series are reconstructed into stationary and fluctuating components to reduce the computational scale. At the same time, the Pearson correlation coefficient is used to screen feature variables and delete feature redundancy, and a support vector regression model (GWO-SVR) optimized by gray wolf algorithm and a long short term memory neural network are established to predict the stationary component and fluctuation component respectively. Finally, an experiment was conducted using the electricity load of an area in Xi'an from 2018 to 2020 as an example. The experiment proves that the accuracy of this model is as high as 94.7%, and the MAPE error is reduced to 2.98%, indicating good accuracy and applicability.

Key words: short-term prediction, variational modal decomposition, sample entropy, fluctuation component, stationary component, GWO-SVR, short and long term memory neural network

Bilin SHAO, Danyang JI. Multi-feature Short-term Prediction of Power Load Components Based on VMD-SE[J]. Electric Power, 2024, 57(4): 162-170.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202303085

https://www.electricpower.com.cn/EN/Y2024/V57/I4/162

Figures/Tables 14

Fig.1 GWO-SVR flow chart

Fig.2 Frame structure of LSTM

Fig.3 Overall prediction framework

Fig.4 Power load line diagram

Table 1 Feature correlation

特征变量	相关度	特征变量	相关度	特征变量	相关度
最高温度	0.715	相对湿度	0.122	前一天负荷	0.950
最低温度	0.656	降雨量	0.119	前两天负荷	0.889
平均温度	0.740	工作日	0.147	前三天负荷	0.855

Fig.5 Sample entropy

Fig.6 The VMD-decomposed modal components and residual

Fig.7 Sample entropy of original load and components

Fig.8 Line chart of stationary components

Fig.9 Line chart of fluctuation component

Fig.10 Change of fitness with iteration number

Fig.11 Training and validation loss

Table 2 Comparison of model evaluation indicators

预测模型	R²	E_MAPE/%	E_MAE/MW	E_RMSE/MW
SVR	0.828	5.278	446.76	593.5
GWO-SVR	0.878	4.325	372.51	501.5
PSO-SVR	0.872	4.34	375.02	512.4
ELM	0.843	5.027	433.29	568.2
LSTM	0.922	3.275	296.35	358.2
GWO-SVR+LSTM	0.947	2.98	261.6	348.8

Fig.12 Comparison of model prediction results

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