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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 11
3269-3273

Short-term power load forecasting algorithm based on maximum deviation similarity criterion BP neural network

Luo Yuhui1
Cai Yanguang1
Qi Yuanhang2
Huang Helie1
1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
2. University of Electronic Science & Technology of China, Zhongshan Institute, Zhongshan Guangdong 528402, China
DOI: 10.19734/j.issn.1001-3695.2018.05.0293

Abstract

This paper proposed a short-term power load forecasting algorithm based on maximum deviation similarity criterion BP neural network to solve the problem of strong randomness, low stability and poor prediction accuracy of enterprise power load. This method first modified the maximum deviation similarity criterion algorithm, and proposed to use the load feature vector of the forecast day and the distance of the center load characteristic after clustering to determine the similar day class of the forecast day. Then, it used the similar daily class load data after clustering as the training data of BP network, and output the load of three consecutive days for 96 points. The experiment shows that the proposed short-term power load forecasting method has great improvement in precision and network training time, and has high effectiveness and practicability.

Foundation Support

国家自然科学基金资助项目(61074147)
广东省自然科学基金资助项目(S2011010005059)
广东省教育部产学研结合项目(2012B091000171,2011B090400460)
广东省科技计划资助项目(2012B050600028,2014B010118004,2016A050502060)
广州市花都区科技计划资助项目(HD14ZD001)
广州市科技计划资助项目(201604016055)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.05.0293
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 11
Section: Algorithm Research & Explore
Pages: 3269-3273
Serial Number: 1001-3695(2019)11-015-3269-05

Publish History

[2019-11-05] Printed Article

Cite This Article

罗育辉, 蔡延光, 戚远航, 等. 基于最大偏差相似性准则的BP神经网络短期电力负荷预测算法 [J]. 计算机应用研究, 2019, 36 (11): 3269-3273. (Luo Yuhui, Cai Yanguang, Qi Yuanhang, et al. Short-term power load forecasting algorithm based on maximum deviation similarity criterion BP neural network [J]. Application Research of Computers, 2019, 36 (11): 3269-3273. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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