@inproceedings{ebcca05573d247618224eecdcd306d3c,
title = "Wind Speed Time Series Modeling under Least Square Error and Genetic Algorithm",
abstract = "This paper presents a mathematical model for the representation of wind speed modeling. This model consists of two parts: the deterministic part, the autoregressive sub-section, and the stochastic model part, which is the moving average sub-section with the time series autoregressive moving average (ARMA) model structure. The problem of the ARMA model structure that will be solved is to find the model parameters and generate the NID signal that gives the best model accuracy. The method used to find the optimal model parameters is the search for optimal solutions using genetic algorithm techniques. The fitness function used is a residual error which the mean square error can express. Simulation is done with 5000 cycles of training. The parameters obtained were tested with training data which gives an MSE value of 0.1868, while the one tested using data outside of training gives an MSE value of 0.1970. Therefore, this model is quite feasible to predict wind speed with an MSE difference of about 5.4%.",
keywords = "ARMA, genetic algorithm, least square error, time series, wind speed modeling",
author = "Jami'in, {Mohammad Abu} and Mardi Santosa and Ii Munadhif and Adhitya, {Ryan Yudha} and Zindhu Maulana and Adiatmoko, {Mohammad Fajar}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 8th IEEE Information Technology International Seminar, ITIS 2022 ; Conference date: 19-10-2022 Through 21-10-2022",
year = "2022",
doi = "10.1109/ITIS57155.2022.10010011",
language = "English",
series = "Proceeding - IEEE 8th Information Technology International Seminar, ITIS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "74--79",
booktitle = "Proceeding - IEEE 8th Information Technology International Seminar, ITIS 2022",
address = "United States",
}
