Advanced AI-Aided Modeling and Planning of Rooftop Wind Energy Generation Systems in Urban Environment

Abstract

Integrating rooftop wind turbines (RWTs) into urban environments presents a substantial opportunity for decentralized renewable energy. Still, it is hindered by the intricate aerodynamic interactions induced by urban structures. This investigation establishes an AI-assisted framework for planning the placement of RWTs in urban environments. The MiDaS depth estimation model is employed to convert 2-D satellite imagery into a detailed 3-D terrain model, which is necessary to analyze wind flow accurately. This process captures the geometries of buildings. Machine learning techniques are employed to analyze and generate wind data from NASA POWER at different hub heights, enabling accurate wind speed prediction. The prevailing wind patterns are identified through wind speed distributions and directional analyses. At the same time, the velocity deficits and disturbances caused by urban obstacles are quantified using an updated Jensen multiwake model. The proposed methodology has been applied and validated in two Indian cities, Barmer and Jaipur, Rajasthan, India. The results indicate that there were substantial power losses and reductions in wind speed. Building-induced wakes, modeled via the Jensen multiwake approach, reduced turbine output and caused significant seasonal fluctuations in capacity factors: Barmer—12.24% loss (2370→2080 kWh); Jaipur—17.53% loss (1540→1270 kWh). Comparative analyses underscore the critical impact of urban morphology on RWT performance. The proposed methodology is a data-driven, robust instrument for optimizing turbine placement, intending to improve the efficiency and reliability of urban wind energy systems. Consequently, it contributes to sustainable urban energy planning and decentralized power generation.