Wind speed forecasting using multivariate time-series radial basis function neural network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An accurate wind information forecasting plays the significant role for wind power system. However, the intermittent characteristic wind speed in nature over the time and from one location to another makes it hard to estimate the usagefactor o fwind farms. Therefore, actual long and short durationforecasting o fwind speed is necessary for wind power generation system efficiency. In this research, wepropose the method toforecast the wind speed data based on weather parameters including, temperature, sea level pressure, dew point, visibility, station pressure, rain intensity, optimum windspeed, maximum temperature, minimum temperature, hail intensity and thunder intensity data. All parameters were predicted using time series model, then the result o fpredicted data was implemented to predict the wind speed data. This research implemented radial basis function neural network (RBF NN) to predict the wind speed and the results were compared to univariate time series forecasting and Least Square Support Vector Machine (LS SVM) algorithm. The result experimentally express better forecasting using RBF NN compared to two other models on the measures of MAPE, MSE and correlation coefficient.

Original languageEnglish
Title of host publication2018 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages423-428
Number of pages6
ISBN (Electronic)9781728101354
DOIs
Publication statusPublished - 17 Jan 2019
Event10th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018 - Yogyakarta, Indonesia
Duration: 27 Oct 201828 Oct 2018

Publication series

Name2018 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018

Conference

Conference10th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018
Country/TerritoryIndonesia
CityYogyakarta
Period27/10/1828/10/18

Keywords

  • Multivariate
  • Radial basis function network
  • Time series

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