An ensemble learning approach on Indonesian wind speed regression

Herley Shaori Al-Ash, Mutia Fadhila Putri, Aniati Murni Arymurthy, Alhadi Bustamam

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

Abstract

As the need of a clean and sufficient electrical power such as generated by wind power has become one of the national goals stated by the Indonesian government, great research interest has been shown towards renewable and energy resources. One of the most vital factors determining the location of wind turbine power installation is the wind speed within a particular area. In this research, wind speed regression using several regressors was performed. Among all of the regressors, AdaBoost regressor presents a significant result by scoring 0.19 as the smallest average mean square value compare to other regressors. The further result yields that AdaBoost regressor acceptable performance occurred at Sulawesi island confirming the wind power generator construction in Sulawesi island was the right decision.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages76-80
Number of pages5
ISBN (Electronic)9781728121338
DOIs
Publication statusPublished - 1 Jul 2019
Event12th International Conference on Information and Communication Technology and Systems, ICTS 2019 - Surabaya, Indonesia
Duration: 18 Jul 2019 → …

Publication series

NameProceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019

Conference

Conference12th International Conference on Information and Communication Technology and Systems, ICTS 2019
Country/TerritoryIndonesia
CitySurabaya
Period18/07/19 → …

Keywords

  • Big data
  • Ensemble learning
  • Indonesian wind speed regression

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