Estimation of Urban Air Temperature From a Rural Station Using Remotely Sensed Thermal Infrared Data

Schuch, Fernanda; Marpu, Prashanth Reddy; Masri, Dina; Afshari, Afshin

doi:10.1016/j.egypro.2017.12.720

Cited by 7 publications

(1 citation statement)

References 26 publications

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“…In this figure, the n Output box show the number of inputs and outputs and th box shows the number of neurons. To put the performance of the created ANN in perspect model for predicting monthly mean air temperature using r To put the performance of the created ANN in perspective, Sahin's (2012) [46] ANN model for predicting monthly mean air temperature using remote sensing had an RMSE of 1.254 • C and Schuch et al's (2017) [47] ANN model had an MAE in the range of 1.3 • C to 2.3 • C. Moreover, regression models used to map the air temperature of hot summer days [13] and daily mean air temperature [12] had an RMSE of 2.31 • C and an RMSE in the range between 1.5 • C and 3.2 • C, respectively. As such, it can be said that the performance of the ANN model created for this study is acceptable and the method described can be used to generate the air temperature in the urban area using weather data from an airport weather station.…”

Section: Ann Models For Urban Air Temperature and Wind Speed Predictionmentioning

confidence: 99%

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

Tariku

Mombeni

2023

Energies

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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.

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Section: Ann Models For Urban Air Temperature and Wind Speed Predictionmentioning

confidence: 99%

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

Tariku

Mombeni

2023

Energies

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Optimization of urban design/retrofit scenarios using a computationally light standalone urban energy/climate model (SUECM) forced by ERA5 data

Afshari

2023

Energy and Buildings

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Urban air temperature model using GOES-16 LST and a diurnal regressive neural network algorithm

Hrisko

Ramamurthy

et al. 2020

Remote Sensing of Environment

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Estimation of Urban Air Temperature From a Rural Station Using Remotely Sensed Thermal Infrared Data

Cited by 7 publications

References 26 publications

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

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

Optimization of urban design/retrofit scenarios using a computationally light standalone urban energy/climate model (SUECM) forced by ERA5 data

Urban air temperature model using GOES-16 LST and a diurnal regressive neural network algorithm

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