基于GRU神经网络的WGAN短期负荷预测模型*

doi:10.11985/2022.02.019

摘要/Abstract

摘要：

为了提高短期负荷预测模型的精度,提出了一种基于门控循环单元(Gated recurrent unit,GRU)神经网络的Wasserstein生成对抗网络(Wasserstein generative adversarial network,WGAN)短期负荷预测模型。将Wasserstein距离作为生成对抗网络(Generative adversarial network,GAN)的损失函数,与传统GAN相比,可以解决其训练过程中存在的梯度消失、模式崩溃等问题。同时,其生成器和判别器模型采用GRU神经网络,用以解决循环神经网络中存在的梯度问题。通过与GRU神经网络模型、传统GAN模型、采用KL散度作为损失函数且生成器和判别器采用GRU的GAN模型进行对比试验,证明了所提出的新模型具有更好的预测精度和稳定性。

关键词: 短期负荷预测, 生成对抗网络, Wasserstein距离, GRU神经网络, 电力系统

Abstract:

To improve the accuracy of short-term load forecasting, a short-term load forecasting model of WGAN based on GRU neural network is proposed. The Wasserstein distance is taken as the loss function of GAN, compared with the traditional GAN, it can solve the problems of gradient disappearance and mode collapse in the training process. At the same time, its generator and discriminator model uses GRU neural network to solve the gradient problem in recurrent neural network. Compared with GRU neural network model, traditional GAN model, and GAN model with KL divergence as loss function and GRU as generator and discriminator, it is proved that the new model has better prediction accuracy and stability.

Key words: Short-term load forecasting, GAN model, Wasserstein distance, GRU neural network, power system

中图分类号:

TM715

高翱, 王帅, 韩兴臣, 张智晟. 基于GRU神经网络的WGAN短期负荷预测模型^*[J]. 电气工程学报, 2022, 17(2): 168-175.

GAO Ao, WANG Shuai, HAN Xingchen, ZHANG Zhisheng. Short-term Load Forecasting Model of WGAN Based on GRU Neural Network[J]. Journal of Electrical Engineering, 2022, 17(2): 168-175.

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输入	学习率α=0.1;权重裁剪参数c=0.01; 批量值m=64;n=5;w为判别器参数; θ为生成器参数
1	while θ do
2	for t=0,1,…,n do
3	样本$\left\{x^{(i)}\right\}_{i=1}^{m} \sim P_{r}$为真实数据
4	样本$\left\{g\left(z^{(i)}\right)\right\}_{i=1}^{m} \sim P_{g}$为生成数据
5	$\mathrm{d} w \leftarrow \nabla w\left[\frac{1}{m} \sum_{i=1}^{m} f_{w}\left(x^{(i)}\right)-\frac{1}{m} \sum_{i=1}^{m} f_{w}\left(g\left(z^{(i)}\right)\right)\right]$ 计算判别器梯度
6	w←w+a·dw判别器参数梯度更新
7	w←clip(w,-c,c) 将判别器参数梯度更新的数值控制在(-c, c)之间
8	end for
9	$\mathrm{d} \theta \leftarrow-\nabla \theta\left[\frac{1}{m} \sum_{i=1}^{m} f_{w}\left(g\left(z^{(i)}\right)\right)\right]$计算生成器梯度
10	θ←θ-dθ生成器参数梯度更新
11	end while

预测模型	E_MAPE(%)	E_max(%)	ME/MW	训练耗时/s
模型1	1.2	3.7	32.67	766
模型2	2.2	4.96	63.27	492
模型3	1.89	3.87	54.69	970
模型4	1.62	3.85	47.76	892

星期	模型1
星期	E_MAPE(%)	E_max(%)	ME/MW
周一	1.87	4..03	53.46
周二	1.32	3.75	39.05
周三	1.28	3.76	37.04
周四	1.25	3.63	36.84
周五	1.23	3.74	35.73
周六	2.05	4.2	60.03
周天	2.0	4.17	58.05
平均值	1.57	3.90	45.74

星期	模型1
星期	E_MAPE(%)	E_max(%)	ME/MW
周一	1.94	3.89	56.81
周二	1.43	3.29	41.29
周三	1.37	3.66	42.53
周四	1.12	3.79	30.16
周五	1.45	3.87	42.64
周六	2.0	4.35	58.67
周日	2.39	4.2	68.15
平均值	1.67	3.86	48.61