Date of Award


Document Type


Degree Name

Master of Science (MS)


Mechanical and Civil Engineering

First Advisor

Hamidreza Najafi

Second Advisor

Troy Nguyen

Third Advisor

Aldo Fabregas Ariza

Fourth Advisor

Ashok Pandit


The study of energy consumption in buildings is an important area of research given the large share of building sector in the worldwide energy usage. The energy consumption of buildings has a high impact on carbon emissions, a greenhouse gas which has proved to play a major role in global warming. In the last decades, global warming has become an increasing concern and worldwide high energy demand have awaken a pressing quest towards improving building energy efficiency. Reducing buildings’ energy usage by implementing energy efficiency measures is one way towards a sustainable future for any country in the world. This thesis, presents a case study of a complete retrofit of a small office building located in a hot and humid subtropical climate zone in Melbourne, FL. The building was constructed in 1961 and without any subsequent energy renovations ever since it was built. The building was fully retrofitted into a zero energy building (ZEB) through a grant from Florida Department of Agriculture and Consumer Services (FDACS) between 2017-2020. This work first looks into a detailed energy model of the building via eQUEST which is used to predict the energy consumption of the building under various conditions. The simulation results for monthly energy consumption are compared against the bill data to understand the strengths and weaknesses of the model. A neural network based model is then developed in MATLAB using simulation data from eQUEST to explore the possibility of using machine learning algorithms for building energy consumption forecasting. Results from this work aims to serve as a baseline for future projects in similar buildings and climatic zones. This work intends to serve as an example for improving energy efficiency in buildings and indoor environmental air quality in buildings, and at the same time reduce carbon footprint


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