Intraday wind power forecasting employing feedback mechanism

The increasing thrust of the world to reduce the dependency on the fossil fuel-based sources has given rise to the high penetration of Renewable Energy Sources (RES). The major RES that are increasing on the large scale throughout the world are solar and wind energy. However, at the grid level, these technologies present a major drawback due to variable energy on account of intermittency and unpredictability. This study presents an intraday forecasting of wind power using the Autoregressive Integrated Moving Average model and wavelet decomposition. The forecasting interval of 15 min has been used for forecasting employing two data sets: first the actual wind power and second the error wind power, generated form NIWE forecasting model. The results were computed for 106 Wavelet-ARIMA cases to obtain the best wavelet model for the wind forecasting problem. The interval of past data blocks for forecasting has also been optimally worked alongside the discussion of the best wavelet for the forecasting problem. The study develops the concept of wavelet decomposition in-depth along with the feedback mechanism that improves the ability of the existing forecasting models with respect to following of actual wind power curve.