Open AccessWind speed prediction using extreme learning machine and neural network for resolving uncertainty in microgrids
Author(s) -
A Seprijanto,
Mat Syaiin,
Dimas Fajar Uman Putra,
Nasyith Hananur Rohiem,
Novian Patria Uman Putra,
Md. Shirajum Munir
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1010/1/012033
Subject(s) - wind speed , wind power , mean absolute percentage error , artificial neural network , renewable energy , extreme learning machine , meteorology , energy (signal processing) , computer science , environmental science , engineering , artificial intelligence , statistics , mathematics , physics , electrical engineering
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.