Abstract

ptimal implementation of urban wind energy contributes towards the development of sustainable cities. This paper focuses on generating an adequate database to use with artificial intelligence (AI) tools to improve the generation of urban wind energy. The paper presents wind tunnel results for square, rectangular, U-shaped, T- shaped, L-shaped buildings and some measurement points in various city configurations. Moreover, the effect of building shapes on turbine street level locations was elaborated using validated CFD literature results on pedestrian level wind conditions. Using these results and literature review from the past decade, a decisional flow chart approach was developed, allowing a preliminary assessment of the modification of upstream wind ve- locities due to urban parametric conditions. Expert and artificial neural network (ANN) systems were built and tested on city configurations with their results compared with those from wind tunnel measurements. The ANN system shows better predictive values than the expert system, with up to 99% success rate. AI programs with the decisional flow chart approach may be used for the identification and assessment of potential turbine locations to maximize the production of urban wind energy.