Degree Type

Thesis

Date of Award

2020

Degree Name

Doctor of Philosophy

Department

Civil, Construction, and Environmental Engineering

Major

Civil Engineering ( Intelligent Infrastructure Engineering )

First Advisor

Kristen Cetin

Abstract

Heatwaves are growing in intensity, duration, and frequency. Such extreme events can adversely affect society’s resiliency by causing mortality, infrastructure damage, and increase in electricity demand and grid failure. One of the main reasons of power outages during extreme heat events is the high power demands on the grid. Such high power demands occur on the electric grid during extreme heat events mainly due to buildings’ cooling demand. Knowing that building performance is impacted from such extreme weather conditions, an accurate evaluation and prediction of electricity consumption and demand prediction at the city scale during heatwave conditions is essential. As such, this study focuses on improving residential building electricity consumption and demand prediction at the city scale by a) developing a novel data-driven modeling approach for city-scale energy modeling and b) proposing a methodology to utilize high resolution weather data with the developed city-scale energy modeling method. Three objectives were investigated in this study. First, a Genetic Algorithm-Based Numerical Moment Matching (GA-NMM) method was utilized to predict the electricity consumption of a large dataset of single family homes through utilizing key features in energy audit and assessors data. This method significantly reduced computational complexity by reducing the sample size while the characteristics of the population is maintained. The applicability of the proposed model is tested with measured data and the results indicated that proposed method performed within the acceptable range and this model can be used to demonstrate the energy behavior of a large set of single family homes. Second, the proposed city scale energy modeling technique was applied to present a quantitative approach for predicting city-scale electricity demand and consumption, and to assess the potential electricity saving and demand reduction for several energy efficiency retrofits. The results indicated that attic insulation improvement can help shift air conditioning operation from peak to off-peak hours. The impact of improving the efficiency of cooling systems on reducing both annual electricity use and peak demand was also quantified. Third, improvement of other aspects of city scale energy modeling by assessing the uncertainty in energy modeling associated with weather data inputs. The results show that the maximum hourly demand difference using different weather data inputs is significant. The objectives that were addressed in this study significantly reduce the computational efforts for city scale energy modeling without compromising the quality since the input for both building characteristics and weather data of the model are localized.

DOI

https://doi.org/10.31274/etd-20210114-69

Copyright Owner

Elham Jahani

Language

en

File Format

application/pdf

File Size

138 pages

Available for download on Friday, January 07, 2022

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