Reliable wind forecasting for grid stability using seasonal ARIMA

Accurate forecasting of wind power generation is essential for ensuring the stable operation of modern electricity grids, particularly as renewable energy penetration continues to grow. However, wind is highly stochastic in nature hence prediction of its availability becomes challenge. To address this issue, the present work employs a Seasonal Autoregressive Integrated Moving Average (SARIMA) based forecasting framework that captures both short-term variations and seasonal trends in wind patterns. The model uses historical wind speed and related meteorological data to generate reliable short-term forecasts. A case study data from the western region of Maharashtra, India, serves as the basis for training and validating the model. A half hourly wind speed sample data throughout the different time-zone of a day is considered to check the robustness of the proposed model. Moreover, a standard performance metrics is used to evaluate the significant enhancement of prediction accuracy. The same is compared with the existing algorithms such as Gated Recurrent Unit and Long Short-Term Memory. The results obtained from the proposed method presents the forecasting accuracy to 90% in optimizing wind power generation and planning.