Open AccessForecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models
Author(s) -
Jorge Luis Tena-García,
Erasmo Cadenas Calderón,
Gilberto González-A,
Eduardo Rangel Heras,
Alain Mbikayi Tshikala
Publication year - 2019
Publication title -
advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1177/1687814018813464
Subject(s) - autoregressive model , autoregressive integrated moving average , wind power , sample (material) , power (physics) , nonlinear autoregressive exogenous model , turbine , sample size determination , mean squared error , wind power forecasting , control theory (sociology) , wind speed , energy (signal processing) , time series , artificial neural network , computer science , statistics , econometrics , electric power system , engineering , meteorology , mathematics , artificial intelligence , control (management) , chemistry , physics , mechanical engineering , chromatography , electrical engineering , quantum mechanics
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom