Short-term wind forecasting using artificial neural networks (ANNs)

The integration of wind farms in power networks has become an important problem. As electricity cannot be preserved because of the highest cost of storage, electricity production must following market demand, necessarily. Short-long term wind forecasting over different time steps is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based on the valid assumption that all the causative factors are implicitly accounted for in the sequence of occurrence of the process itself. Hence, time series modelling is equivalent to physical modelling. Artificial neural networks (ANNs), which perform a non-linear mapping between inputs and outputs, provide a robust approach for wind prediction. In this work, these models are developed for simulating wind speed and energy production of a wind farm with three wind turbines, comparing different prediction temporal periods. We applied artificial neural networks for short and long term load forecasting using real load data.