The application of neural network in wind speed forecasting

Shih Hua Huang, Ko Ming Mu, Ping Yuan Lu, Chao Yang Tsao, Yih-Guang Leu, Li Fen Chou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Usually, a typical wind turbine system cannot generate electricity power consistently, because the power outputs are heavily depended on wind speed. However, terrain, temperature, humidity and other factors can also affect wind speed. Therefore, wind power forecasting is a complex, multi-dimensional, and highly non-linear system. Neural network is able to learn the relationship between system inputs and outputs without mathematical conversion, and perform complex non-linear mapping, data classification, knowledge processing, and so forth. In addition, neural network also has the ability of parallel processing to reduce computing time, so it is suitable for wind power forecasting. The purpose of this paper is to use neural network technology to design a wind power forecasting system. Moreover, the efficiency analysis of the proposed wind power forecasting system in Kinmen farm is described. Finally, we use MATLAB to implement the proposed wind power forecasting system in Kinmen farm, which is capable of forecast within 48-hours ahead.

Original languageEnglish
Title of host publicationICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages366-370
Number of pages5
ISBN (Electronic)9781479980697
DOIs
Publication statusPublished - 2015 Jun 1
Event2015 12th IEEE International Conference on Networking, Sensing and Control, ICNSC 2015 - Taipei, Taiwan
Duration: 2015 Apr 92015 Apr 11

Publication series

NameICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control

Other

Other2015 12th IEEE International Conference on Networking, Sensing and Control, ICNSC 2015
CountryTaiwan
CityTaipei
Period15/4/915/4/11

Keywords

  • Kinmen farm
  • neural network
  • power forecasting
  • wind

ASJC Scopus subject areas

  • Instrumentation
  • Control and Systems Engineering
  • Computer Networks and Communications

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