Method selection in different regions for short-term wind speed prediction in Japan

Ikki Tanaka, Hiromitsu Ohmori

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

8 Citations (Scopus)

Abstract

Recently, many approaches have been proposed in wind speed prediction. However they are verified only in certain areas, and have not been a quantitative verification in many different locations. Therefore, this paper used the data of the various parts of Japan for the one-step-ahead prediction, and applied a number of different approaches to each point. And then it was evaluated such as MAE. These models are persistent model, ARMA-GARCH model, non-linear autoregressive network with an external input (NARX), recurrent neural network (RNN), and support vector regression (SVR). From the results of the numerical simulation at each point, this paper presents the results that it is difficult to create the same model which minimize the error in all areas, and there is a need to create a predictor for each region.

Original languageEnglish
Title of host publication2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages189-194
Number of pages6
ISBN (Print)9784907764487
DOIs
Publication statusPublished - 2015 Sep 30
Event54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015 - Hangzhou, China
Duration: 2015 Jul 282015 Jul 30

Other

Other54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015
CountryChina
CityHangzhou
Period15/7/2815/7/30

Keywords

  • ARMA
  • GARCH
  • non-linear autoregressive network with an external input (NARX)
  • prediction model
  • recurrent neural network (RNN)
  • support vector regression (SVR)
  • Wind speed

ASJC Scopus subject areas

  • Control and Systems Engineering

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