Short-term Wind Forecasting Using Artificial
Neural Networks (ANNs)
Author(s)
M. G. De Giorgi, A. Ficarella & M. G. Russo
Abstract
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.
Keywords: neural artificial networks (ANNs), forecasting wind, turbine, CFD.
Keywords
neural artificial networks (ANNs), forecasting wind, turbine, CFD
Related Book
Other papers in this volume
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