自编码器与PSOA-CNN结合的短期负荷预测模型

doi:10.6040/j.issn.1671-9352.0.2018.715

摘要/Abstract

摘要：

提出了一种自编码器与PSO算法优化卷积神经网络结合的电力系统短期负荷预测模型。首先利用自编码器对相关变量数据进行处理,降低所需数据的噪声变量,提高预测效率;然后利用粒子群算法对卷积神经网络的权值和阈值进行优化,可有效提高预测模型的预测精度和预测速度。通过对实际电网的负荷数据进行仿真,验证了模型具有较高的预测精度。

关键词: 卷积神经网络, 自编码器, 粒子群优化算法, 短期负荷预测

Abstract:

A short-term load forecasting model which combines the autoencoder and convolutional neural network optimized by particle swarm optimization is proposed. Firstly, the autoencoder is used to process the relevant variable data, reduce the noise variable of the required data, and improve the prediction efficiency. Then particle swarm optimization is used to optimize the weight and threshold of the convolutional neural network., which can effectively improve the prediction accuracy and prediction speed of the prediction model. By simulating the load data of the actual power grid, it is verified that the proposed model has higher prediction accuracy.

Key words: convolutional neural network, autoencode, particle swarm optimization, short-term load forecasting

中图分类号:

TM715

王文卿,撖奥洋,于立涛,张智晟. 自编码器与PSOA-CNN结合的短期负荷预测模型[J]. 《山东大学学报(理学版)》, 2019, 54(7): 50-56.

Wen-qing WANG,Ao-yang HAN,Li-tao YU,Zhi-sheng ZHANG. Short-term load forecasting model based on autoencoder and PSOA-CNN[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2019, 54(7): 50-56.

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模型	平均绝对误差/%	最大相对误差/%
常规CNN	3.93	10.12
自编码器与PSOA-CNN结合	1.87	5.95

日期	平均绝对误差/%		最大相对误差/%
日期	模型1	模型2	模型1	模型2
1	4.25	2.01	10.48	6.31
2	3.93	1.87	10.12	5.95
3	3.25	1.33	9.25	4.78
4	3.16	1.52	9.44	4.14
5	3.37	1.19	8.70	4.25
6	3.67	2.06	8.89	6.19
7	4.31	2.29	10.95	6.72
平均值	3.70	1.76	9.69	5.47

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