基于纵横交叉算法-门控循环单元的日前电价预测模型

doi:10.3969/j.issn.2097-0706.2022.11.005

摘要/Abstract

摘要：

新型电力系统下，新能源渗透率不断提高，电价出现特征数目增多、波动性大的问题，为了提高日前电价的预测精度，提出了一种基于纵横交叉算法（CSO）优化门控循环单元（GRU）的组合预测模型。首先利用互信息（MI）方法分析电价与特征之间的相关性，提取相关性较高的特征，同时利用变分模态分解（VMD）方法将电价序列分解为若干分量，然后将各分量分别叠加提取的特征送入GRU预测模型进行预测，并利用CSO优化模型的部分参数，避免模型陷入局部最优，最后将各分量预测结果叠加得到最终预测电价。选取北欧Nordpool电力市场运营数据进行试验，试验结果表明该方法相比于其他方法具有更高的预测精度。

关键词: 新型电力系统, 新能源, 电价预测, 互信息, 变分模态分解, 纵横交叉算法, 门控循环单元

Abstract:

The penetration rate of renewable energy is rising with the development of new energy system.In view of the mounting number of features and fluctuation of electricity price， a combined prediction model based on gated recurrent unit （GRU） optimized by crossover optimization algorithm（CSO）is proposed，to improve the prediction accuracy of day-ahead electricity price. Firstly， the correlation between electricity price and features is analysed by mutual information （MI） method and those features with high correlation are extracted. The electricity price sequence is decomposed into several components by variational modal decomposition （VMD）. Features extracted from the superimposed components are sent to GRU prediction model for prediction. CSO algorithm can optimize some parameters of the model and avoid the model from falling into local optimum. The final predicted electricity price is obtained by superimposing the prediction results of different components. Verified by the experimental data of Nordpool， a power market leader in the Nordic region， the proposed prediction method is more accurate than others.

Key words: new power system, renewable energy, electricity price prediction, mutual information, variational mode decomposition, crossover optimization algorithm, gated recurrent unit

中图分类号:

TK01⁺9:TM715

翟广松, 王鹏, 梁鹏勋, 谢智锋, 殷豪. 基于纵横交叉算法-门控循环单元的日前电价预测模型[J]. 综合智慧能源, 2022, 44(11): 36-42.

ZHAI Guangsong, WANG Peng, LIANG Pengxun, XIE Zhifeng, YIN Hao. Day-ahead electricity price prediction model based on GRU optimized by crossover optimization algorithm[J]. Integrated Intelligent Energy, 2022, 44(11): 36-42.

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特征		MI值
生产		0.587 8
负荷		0.415 3
风电		0.206 5
计划流量		0.574 1
市场容量		0.317 9
市场耦合因素	瑞典净交换电量	0.095 8
	挪威净交换电量	0.688 0
	丹麦净交换电量	0.287 6
	芬兰净交换电量	0.148 9
	立陶宛净交换电量	0.104 4
	拉脱维亚净交换电量	0.229 1
	爱沙尼亚净交换电量	0.317 5
	挪威价区2-荷兰交换电量	0.193 5
	瑞典价区4-德国交换电量	0.317 0
	丹麦价区1-德国交换电量	0.320 7
	丹麦价区2-德国交换电量	0.215 4

预测模型	X_RMSE	X_MAE	X_SMAPE
GRU	3.694	2.711	13.149
PCA-GRU	3.297	2.503	11.858
MI-GRU	3.282	2.450	11.696

预测模型	X_RMSE	X_MAE	X_SMAPE
GRU	3.282	2.450	11.696
EEMD-GRU	2.307	1.721	8.286
VMD-GRU	2.064	1.545	7.616

预测模型	X_RMSE	X_MAE	X_SMAPE
BP	2.321	1.736	8.438
RNN	2.529	1.873	9.155
LSTM	2.138	1.623	8.022
GRU	2.064	1.545	7.616
CSO-GRU	2.026	1.523	7.546