ANN-Based Method for Urban Canopy Temperature Prediction and Building Energy Simulation with Urban Heat Island Effect in Consideration

Abstract

The process of urbanization resulting from population growth is causing a transformation of natural landscapes into built environments, and contributing to a significant rise in air and surface temperatures in urban areas, resulting in what is known as the urban heat island (UHI). Ignoring the UHI effect and use of weather data from open fields and airport locations for energy and thermal comfort analysis can lead to over- and underestimation of heating and cooling loads, improper sizing of equipment, inefficiencies in the mechanical systems operation, and occupants’ thermal discomfort. There is a need for computationally efficient urban canopy temperature prediction models that account for the urban morphology and characteristics of the study area. This paper presents the development and application of an artificial neural network (ANN)-based method for generating hourly urban canopy temperature and local wind speed for energy simulation. It was used to predict the urban canopy temperature of a neighborhood in downtown Vancouver and the resulting building energy consumption and indoor temperature in a typical building in the area. The results showed that the UHI effect increased the total cooling energy demand by 23% and decreased the total heating energy consumption by 29%, resulting in an overall negative effect on the total energy demand of the building, which was 18% higher in the urban area. The UHI effect also increased the number of hours of indoor temperature above the cooling set point by 7.6%. The methodology can be applied to determine the urban canopy temperature of neighborhoods in different climate zones and determine the varying urban heat island effects associated with the locations.

Towards a Sustainable Urban Future: A Comprehensive Review of Urban Heat Island Research Technologies and Machine Learning Approaches

“…In 2023, two studies implemented Bayesian Networks in the field of UHI prediction [58,59], while another research used Artificial Neural Networks (ANNs) for Urban Canopy Temperature and UHI prediction [60]. The Bayesian Network research in New Jersey used a data-driven strategy and machine learning techniques to predict UHI severity, demonstrating high performance in accuracy, precision, recall, F1 score, and AUC.…”

“…In 2023, a study presented an advanced method for predicting wind speed and temperature in urban canopies using Artificial Neural Networks (ANNs) [60]. This approach significantly increases the accuracy of building energy simulations by considering local urban climatic conditions.…”

“…The research, implemented in downtown Vancouver, Canada, used air temperature, wind speed, solar radiation, and relative humidity data, along with HOBO U10-003 data loggers. While the model is adaptable to different urban settings, its accuracy varies for various locations [60].…”

Investigation on Air Ventilation within Idealised Urban Wind Corridors and the Influence of Structural Factors with Numerical Simulations

“…Wind corridors with an AR value of about 0.1 could be considered ideal wind corridors or major wind corridors. For most cities, the average canopy height is approximately 30-60 m [45,46]; therefore, a width in the range of 300-600 m is reasonable for major wind corridor planning.…”

The Impact of Architectural Layout on Urban Heat Island Effect: A Thermodynamic Perspective

“…In urban planning, the impact of architectural layout on the UHI effect is particularly crucial as it determines the distribution and flow of urban heat. Therefore, studying the correlation between architectural layout and the UHI effect from a thermodynamic perspective holds important theoretical and practical significance for mitigating the UHI effect and guiding urban planning [7,8].…”