Wind speed forecasting with ARIMA fourier time series model.

Wind is one of the most important sustainable energy sources because it uses the kinetic energy provided by moving air to generate electrical renewable energy. To generate the electrical energy, the wind turbine is used to collect the wind's kinetic energy. To generate more wind power, the wind turbine needs to be located at the areas which have strong wind speed and air density. Hence, realizing the significant of wind speed, the study of forecasting wind speed needs to be done to determine the best location for wind turbine and what early risk management plan can be created for the convenience of power grid dispatcher. In this study, the Autoregressive Integrated Moving Average (ARIMA) Fourier time series model was used to forecast the wind speed at Senai station from the year 1985 to 2000. In the analysis, ARIMA Fourier model was compared with ARIMA model in terms of in-sample and out-sample measurement errors. The best fitted model will have the lowest measurement errors of Mean Absolute Percentage Error (MAPE) and Root Mean Square Absolute Error (RMSE). The results showed that ARIMA Fourier model outperformed the ARIMA model. Thus, ARIMA Fourier model is selected as the best forecasting model as compared to ARIMA for the management plan of wind turbine's site. [ABSTRACT FROM AUTHOR]