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

Mohammad Mortezazadeh,Mirata Hosseini,Senwen Yang,Liangzhu Wang,Jiwei Zou

doi:10.3390/atmos13020214

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).

Highlights

Pollutants, environmental problems, fossil fuel reserves reduction, and rising energy production costs have led the world to turn to clean and renewable energy resources
In order to capture renewable wind power to the greatest extent, besides the classic wind turbines inside the wind farm [4,5,6], various other types of wind turbines have been developed for urban applications, such as building-mounted wind turbines [7], ducted wind turbines [4], and roof-mounted wind turbines [8,9]
A roof-mounted wind turbine is a small wind turbine that can be installed on the roof of buildings in urban areas [8,9]

Summary

Introduction

Pollutants, environmental problems, fossil fuel reserves reduction, and rising energy production costs have led the world to turn to clean and renewable energy resources. There are two common ways to capture accurate wind speed data for the roof of each building in an urban area to estimate rooftop-mounted wind power potential. The first one uses measured data obtained by local weather stations on the building roofs Another approach is based on numerical simulations to provide detailed information, once the simulations are validated, within a selected urban area for estimating the potential of rooftop wind turbine power. The latter is more often applied because of its low costs, especially for a large region [19]. The proposed approach can be applied to other urban areas, and the data obtained are beneficial for building owners, city planners, and policymakers

CityFFD

Findings

Dimensionless Simulation