Abstract: Power system operators have to predict changes in wind power production in order to schedule the spinning reserve capacity and to manage the grid operations. Wind power forecasting plays an important role in the allocation of balance power. Although the prediction accuracy of the wind power forecasting is lower than the prediction accuracy of load forecasting. Wind power forecasts still play a key role to address the operation challenges in the electricity supply. This paper deals with medium term minute wise forecast of wind energy for the state grid of Karnataka by employing a simple time series analysis. . Data collected from August 8th, 9th is used to predict for the august 10th and a template is designed so that by giving any 2 days data 3rd day data can be forecasted automatically.

Keywords: Classical Multiplicative model, Linear Regression, Mean Error (ME), Mean Absolute Error (MAE), Mean Squared Error (MSE), Percentage Error (PE), Seasonality Trend, Mean Absolute Percentage Error (MAPE).