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A Simplified Heat Wave Warning System


A Simplified Heat Wave Warning System

Nakhaie Ashtiani, Arya (2013) A Simplified Heat Wave Warning System. Masters thesis, Concordia University.

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A Simplified Heat Wave Warning System
Arya Nakhaie Ashtiani
Extreme heat is a natural hazard that could rapidly increase in frequency, duration, and magnitude in the 21st century. During the summer, the combined effect of urban heat island (UHI), climate change and global warming increases ambient air temperature. This leads to a rise in indoor environment temperature, reduction of thermal comfort, increase of cooling demand, and heat related morbidity and mortality especially among vulnerable people such as the elderly and those who are living in buildings without mechanical ventilation systems.
Cities are developing tools to predict the indoor air temperature during extreme heat waves in order to be able to provide emergency plans if necessary. To do so, it is required to find a relationship between the indoor and outdoor conditions. Hence there is an urgent need to develop a reliable method for indoor air temperature prediction by taking into consideration not only the outdoor conditions but also the socio-economic aspects of the neighborhood.
The objective of this study is to develop a warning system to predict the indoor air thermal condition during heat wave events in buildings without mechanical ventilation systems. In order to develop a regional heat warning system, two different methods were proposed and tested for an indoor air temperature forecasting application with respect to neighborhood parameters. The first method was based on regression and the second one was based on the Artificial Neural Network (ANN) model. The inputs and outputs to the proposed models were the field measurement data which has been collected on Montreal Island during the summer of 2010 (Park et al, 2010). To find the most practical approach, both proposed models were compared with respect to their accuracy and the required resources. A comparison of the proposed regression and ANN models was conducted by two different levels of simulation. The ANN model showed better accuracy in predicting the indoor dry-bulb temperature, but it was more complicated to apply.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:Nakhaie Ashtiani, Arya
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Building Engineering
Date:April 2013
Thesis Supervisor(s):Haghighat, Fariborz
ID Code:977395
Deposited On:18 Nov 2013 16:39
Last Modified:18 Jan 2018 17:44
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