Estimating Urban Wind Speeds and Wind Power Potentials Based on Machine Learning with City Fast Fluid Dynamics Training Data

Abstract

Wind power is known as a major renewable and eco-friendly power generation source. As a clean and cost-effective energy source, wind power utilization has grown rapidly worldwide. A roof-mounted wind turbine is a wind power system that lowers energy transmission costs and benefits from wind power potential in urban areas. However, predicting wind power potential is a complex problem because of unpredictable wind patterns, particularly in urban areas. In this study, by using computational fluid dynamics (CFD) and the concept of nondimensionality, with the help of machine learning techniques, we demonstrate a new method for predicting the wind power potential of a cluster of roof-mounted wind turbines over an actual urban area in Montreal, Canada. CFD simulations are achieved using city fast fluid dynamics (CityFFD), developed for urban microclimate simulations. The random forest model trains data generated by CityFFD for wind prediction. The accuracy of CityFFD is investigated by modeling an actual urban area and comparing the numerical data with measured data from a local weather station. The proposed technique is demonstrated by estimating the wind power potential in the downtown area with more than 250 buildings for a long-term period (2020–2049).

Keywords

• CityFFD
• climate change
• machine learning
• roof-mounted wind turbines
• wind power potential