Behrad Bezyan

I am developing Machine Learning and Deep Learning models for evaluation the energy performance to detect and diagnose the simultaneous multiple faults in the HVAC systems of the commercial buildings which accounts for about 40% of the total energy use in the US. I use Python with Scikit-learn, TensorFlow, PyTorch, and Keras libraries for the development of models, as my PhD thesis. Also, I am a data scientist skilled in programming with Python and SQL (pgAdmin) for data analysis.
Moreover, I Developed machine learning models using historical data of HVAC system of two semi-detached houses located in Inuvik, NWT, Canada and weather data for prediction of heating energy use and developed the Principal Component Analysis (PCA) model for fault detection and diagnosis scope in the HVAC system as my MASc thesis.

I have been working as a Data Scientist (Intern) at Paper Co. an E-learning company with the focus on the development of machine learning models such as PCA models for clustering and estimation the demands with respect to the week-days, and grades distribution, and also scheduling the required number of tutors for each time slot with needed teaching expertise. 

I am defending my PhD thesis in Building Engineering at Concordia University under the supervision of Professor Zmeureanu in March 2022. My PhD thesis title is the multiple faults detection and diagnosis in the HVAC system using Machine Learning and Deep Learning models.

During my PhD research, I developed various supervised and unsupervised Machine Learning models with regression and classification-based approaches for the prediction of the targets and detection of faults consecutively. Moreover, for the fault diagnosis approach, I have developed deep learning models such as Recurrent Neural Networks using long-short-term memory (LSTM) and Deep Artificial Neural Networks with the combination of rule-based techniques. For the development of these models, I used Python programs with the use of open-source packages, such as Scikit-learn, Keras, and Tensorflow.  

Moreover, I am a holder of MASc degree in Building Engineering which was completed in 2018 at Concordia University under the supervision of Professor Zmeureanu, with the research topic of “Evaluation of the energy performance of ongoing commissioning houses in northern Canada (Inuvik, NWT) and detection the faults in space heating and domestic hot water using the supervised and unsupervised Machine Learning models. 

I am also a holder of a BSc degree in Mechanical Engineering and implemented my capstone research on the analysis of Geothermal Heat Pumps with various pipe configurations, and proposed novelties in the development of more energy efficient pipe configurations. 

PhD. Building Engineering

(Engineering, Data Science and Machine Learning projects)

Supervisor: Dr. Radu Zmeureanu

MASc. Building Engineering

(Engineering, Data Science and Machine Learning projects)

Supervisor: Dr. Radu Zmeureanu

BSc. Mechanical Engineering

(Mechanical Engineering)