Two-Stage Artificial Neural Network Model for Short-Term Load Forecasting

被引：16

作者：

Hsu, Yuan-Yu ^{[1
]}

Tung, Tao-Ting ^{[2
]}

Yeh, Hung-Chih ^{[3
]}

Lu, Chan-Nan ^{[1
]}

机构：

[1] Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung, Taiwan

[2] Taiwan Semicond Mfg Co Ltd, Facil Dept, Tainan, Taiwan

[3] Taiwan Power Co, Syst Operat Dept, Taipei, Taiwan

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 28期

关键词：

Short-term load forecast; power system operation; artificial neural network; load adjustment; day-ahead electricity market;

D O I：

10.1016/j.ifacol.2018.11.783

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Short-term load forecast (STLF) is important to ensure stable, reliable and efficient power system operations. In this paper, we propose a two-stage artificial neural network (ANN) model for load forecasting application. The proposed system is currently being tested in the Taiwan Power Company (TPC) with potential for future adoption in their decision support systems. The accuracy of the proposed forecast model is tested using the historical data obtained from TPC; the results show that the proposed two-stage ANN model can outperform a previously proposed single stage ANN load forecast model. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：678 / 683

页数：6

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