A distribution fuzzy inference and neural networks based model applied in wind speed forecasting

Increasing electricity demand gives a rise to the necessity of cleaner and safer electric supply. Short-term-related time series prediction in electricity power system with high precision is irreplaceable in the efficient management of electric systems. However, it is both a challenging and significant task to achieve the accurate prediction of short-term-related time series. Many models lack stability and ignore the importance of meteorological factors, which lead to poor prediction accuracy. This paper develops a reliable numerical model for verification based on fuzzy inference and meteorological factors (atmospheric temperature, atmospheric pressure and atmospheric density). The fuzzy neural networks are a favourable scheme in electricity power system time series predictions mainly due to their endogenous capacity of robust modelling of data sets with highly non-linear relationship between inputs and outputs. Three experiments covering the data collected from Hebei are performed to verify the effectiveness of the proposed hybrid model by comparing it with three well-known methods. It is concluded that the hybrid models proposed not only can satisfactorily approximate the actual value but they also can be an effective tool in the planning and dispatching of smart grids.