Dissertations and Theses

Date of Award


Document Type



Mechanical Engineering

First Advisor

Prathap Ramamurthy

Second Advisor

Feridun Delale


Energy Consumption, Building, COVID-19, New York City


Since February 2020, the spread of COVID-19 affected the world economy, with the population of people contacting the virus in New York City at that time being at the highest in the United States of America. Thus, the need for remote/online learning was adopted for the safety of faculty, staffs, and students of New York Educational institution like CCNY. In the study, I focused on energy consumption as regards to COVID and POST-COVID periods in New York City (NYC), since most employees are working from home and schools are online which implies that most commercial buildings are vacant. Also due to the high rate of COVID-19 cases in NYC, some people relocated to other places with lower cases. Therefore, I considered how this affects energy consumption of both commercial and residential buildings.

Furthermore, I obtained data from the New York City Department of Planning database also known as PLUTO (Public Land Use and Tax Lot database) for building energy consumption in NYC before COVID and compared it to data retrieved during COVID, then I predicted what will happen to post-COVID building energy consumption. I analyzed these data using data analyzing software like MATLAB, Python, Excel etc. For my stimulations on EnergyPlus, I could not get data for NYC, but I had to use data retrieved in Chicago, Illinois which is relatively close to that of NYC. I was able to give my prediction and recommendations based on the correlation that was obtained from the analysis. Also, I briefly discussed how the NYC governing council can help solve any potential problems or put policies in place to help improve building energy saving mechanism. I also gave suggestions on how occupants of buildings especially residential buildings can adopt energy saving behaviors to help save cost of energy consumption.

In addition, I used Google Sketchup Pro and Google Earth to get the 3-dimensional model of Steinman Hall including the underground floors. Then, I ran some stimulations with varying population of occupants in the building using Google Sketchup Pro with OpenStudio and EnergyPlus. After I obtained the stimulation results from OpenStudio and EnergyPlus, I analyzed and interpreted the results. I also gave my prediction and recommendations based on the correlation that was obtained from the analysis. Also, I discussed how energy efficient systems like solar panels can be implemented to help improve the building energy saving mechanism.



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