Authors:Christos Ioakimidis, Konstantinos Genikomsakis
A number of location characteristics, such as buildings, mountains, and trees, are likely to influence the wind flow that reaches a microwind turbine located at a residential area, and as a consequence they may affect the actual wind speed that is potentially utilized by the turbine. In this context, simple regional predictions for the wind energy from the nearest location available can easily lead to unacceptable modeling errors. There is thus a need to develop a framework for predicting the values of the wind speed at the desired location. This work addresses the aforementioned issue by employing a multilayer feed-forward back-propagation neural network for classification that utilizes the global forecast system (GFS) predictions on wind speed and direction to identify patterns of the wind behavior at the location considered in order to obtain a stochastic distribution of the daily wind speed. The proposed approach aims to support the implementation of an enhanced energy box (EB) management decision tool, while its feasibility is demonstrated through a case example for a region in the south of Portugal.