Wind speed prediction using extreme learning machine and neural network for resolving uncertainty in microgrids

A. Seprijanto, M. Syai'In, D. F.U. Putra, N. H. Rohiem, N. P.U. Putra, M. Munir

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Wind energy is one of the several types of renewable energy that exist today. however, wind energy has a high degree of uncertainty due to weather effects. Wind speed prediction is needed to determine the energy that wind turbines can produce at each unit. For optimizing wind speed schedulling, the accuracy of wind speed prediction is considered. Extreme learning machine (ELM) and neural network (NN) is implemented to predict hourly wind speed for 24 hour and power generation from wind turbines can produce. Wind speed probability data is taken from sidrap wind farms in indonesia. To determine the performance of wind predictions based on the error value between actual and predicted, mean absolute percentage error (MAPE) is applied.

Original languageEnglish
Article number012033
JournalIOP Conference Series: Materials Science and Engineering
Volume1010
Issue number1
DOIs
Publication statusPublished - 15 Jan 2021
Externally publishedYes
Event2nd International Conference on Advanced Engineering and Technology, ICATECH 2020 - Surabaya, Indonesia
Duration: 26 Sept 2020 → …

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