Electricity load price forecasting based on sparse autoencoder nonlinear auto-regressive network

史大洋; 路峰迎; 李文凯; 弓 帅; 李 垚; 王 欣; 魏文震

doi:10.3969/j.issn.1009-1831.2022.05.005

您当前的位置：

首页 >

文章列表页 >

Electricity load price forecasting based on sparse autoencoder nonlinear auto-regressive network

更新时间：2026-03-05

- Electricity load price forecasting based on sparse autoencoder nonlinear auto-regressive network
- Vol. 24, Issue 5, (2022)
- 作者机构：
  
  1. 山东省建筑设计研究院有限公司,济南,250001
  2. 国网山东省电力公司淄博供电公司,山东,淄博,255000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2022.05.005
  CLC：
- Published：2022
- 稿件说明：
移动端阅览
史大洋, 路峰迎, 李文凯, et al. Electricity load price forecasting based on sparse autoencoder nonlinear auto-regressive network[J]. 2022, 24(5).
DOI：

史大洋, 路峰迎, 李文凯, et al. Electricity load price forecasting based on sparse autoencoder nonlinear auto-regressive network[J]. 2022, 24(5). DOI： 10.3969/j.issn.1009-1831.2022.05.005.

摘要

准确的电价和负荷预测对现代电力系统至关重要，但由于电价与负荷之间存在较强的相关性，若不考虑其相互影响，将导致预测的精度下降。为了提高现有方法的预测准确性，在考虑价格与负荷关系的前提下，提出了一种基于深度递归神经网络的价格与负荷预测模型，即基于外部输入的稀疏自编码器的非线性自回归网络，其功能包括特征提取和预测。首先针对特征提取环节，对原有方法进行改进，提出了稀疏自编码器，可以大大提高特征提取的有效性。其次，利用非线性自回归网络进行电价和负荷预测。使用电力市场大数据ISONE 和 PJM 进行仿真验证，与级联 Elam 网络相比，ESAENARX在负荷预测方面将平均绝对误差降低了16%，在价格预测方面降低了7%。

Abstract

It is essential for modern power system to forecast electricity price and load accurately. However

due to the strong correlation between the electricity price and the load

if the mutual influence is not taken into account

the accuracy of the forecast will be reduced. In order to improve the prediction accuracy of existing methods

price and load relationship are considered and a deep recurrent neural networks model is proposed for price and load forecasting

that is sparse autoencoder nonlinear autoregressive network with exogenous inputs comprising of feature engineering and forecasting. Firstly

an efficient sparse autoencoder is proposed to improve the effectiveness of feature extraction by improving the original method. Secondly

the nonlinear autoregressive network is used to forecast the load and price. The ISONE and PJM big datas of power market are simulated and verified. Compared with cascaded Elman networks

sparse autoencoder nonlinear autoregressive network reduces the mean absolute error by 16% in load forecasting and 7% in price forecasting.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Power supply demand division method for end users based on big data mining

Key technologies of load forecasting in distribution network based on big data platform

Preliminary study on the construction of support platform for power marketing data sharing ecology

Application and practice of ubiquitous power Internet of things in marketing services

Life prediction of electric energy meter based on shared data sources and combined life stress model

Related Author

柳伟

张重阳

王云飞

姜世公

赵冬

胡丹蕾

刘充许

张小斐

Related Institution

南京理工大学自动化学院

国网经济技术研究院有限公司

清华大学电机工程与应用电子技术系

国网河南省电力公司电力科学研究院

国网浙江省电力有限公司电力科学研究院

AI问答

⁰