ARMA Time Series Model for Wind Power Forecasting in Wind Farms

The ARMA (AutoRegressive Moving Average) time series model is a statistical method widely used for time series forecasting applications. In wind farm operations, this model can effectively predict wind power output levels, enabling optimized adjustment of wind turbine generation and related equipment control systems. The forecasting capabilities can be implemented through MATLAB source code that includes functions for parameter estimation using maximum likelihood methods, model order selection via information criteria (AIC/BIC), and residual analysis for model validation. Special case analyses through MATLAB implementations allow researchers to thoroughly examine model performance under specific wind conditions, understand prediction accuracy limitations, and optimize model parameters. The practical implementation involves key MATLAB functions such as arima() for model specification, estimate() for parameter calibration, and forecast() for generating predictions. Therefore, the ARMA time series model combined with MATLAB source code applications holds significant practical value and can be extensively deployed in wind farms and similar renewable energy forecasting scenarios.