Short-term Load Forecasting Method Based on A Novel Robust Loss Neural Network Algorithm

被引：0

作者：

Cai Q. ^{[1
]}

Chao Z. ^{[1
]}

Su B. ^{[1
]}

Wang L. ^{[1
]}

Duan Q. ^{[1
]}

Wen Y. ^{[2
]}

Li B. ^{[2
]}

机构：

[1] Power Dispatching Control Center of Guangdong Power Grid Co., Ltd., Guangzhou

[2] Beijing Tsintergy Technology Co., Ltd., Haidian District, Beijing

来源：

Wen, Yakun (wenyk@tsintergy.com) | 1600年 / Power System Technology Press卷 / 44期

关键词：

Artificial neural network; Exponential loss; Information entropy; Robustness; Short-term load forecast;

D O I：

10.13335/j.1000-3673.pst.2019.2614

中图分类号：

TM72 [输配电技术];

学科分类号：

摘要：

This paper first proposes a measure induced by correntropy (CIM), which has the characteristics of non-convexity, robustness, smoothness, boundedness, approximation behavior, etc. This metric is used as the loss function of the artificial neural networks (ANN) to improve its robustness and then a new robust artificial neural networks framework, exponent loss artificial neural networks(ELANN), is built to reduce the noise and the outliers. The ELANN inherites the advantages of the ANN and improves the predictive performance of the ANN in solving regression problems. The simulation analysis is performed by using the actual load data of Guangdong Power Grid from 2016 to 2018. The results show that this method can effectively improve the accuracy and reliability of load forecasting, and provide a reliable decision basis for the power dispatching departments. © 2020, Power System Technology Press. All right reserved.

引用

页码：4132 / 4139

页数：7

共 39 条

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