Uncertainty analysis of different forecast models for wind speed forecasting

Time-ahead forecasting of renewable energy resources is essential for successful planning and operation of renewable integrated micro grids. Numerous studies have focused on wind energy forecasting; however, most aim to identify the best forecasting model using error metrics. Owing to the highly unpredictable nature of the wind flow, uncertainty associated with these forecasts is also significant. Uncertainty in the forecasts can be analysed and modelled using statistical methods. In this work equal emphasis is given for numerical error metrics as well as statistical modelling of errors. In the first stage, focus is on forecasting wind speed using statistical and artificial intelligence (AI) techniques. Seasonal autoregressive integrated moving average with exogenous factors (SARIMAX), support vector regression (SVR), long short term memory (LSTM), gated recurrent units (GRU) models are used and performance is evaluated using error metrics. Following this, forecast error distribution is studied and uncertainty analysis is carried out using statistical methods.