About Me
I’m a lifelong student who is passionate about energy efficiency and data science. I have over 6 years of experience performing data analyses, applying transformations, and building machine learning models to provide business intelligence solutions. Additionally, I have been working in the commercial building energy and sustainability sector for over 11 years.
pyExpandObjects
I am the lead developer of pyExpandObjects which is a pre-processing tool that maps simplified JSON template objects into complex HVAC system components ready for building energy modeling simulation for the EnergyPlus software package.
EIA Data Analysis and Prediction
When time allows, I work on a tool to extract information from the U.S. Energy Information Administration (EIA) bulk download feature, and perform some general analyses.
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Analyses and Machine Learning Models
Total Energy Dataset Preliminary Check
Prediction of Total Electrical Net Generation From Coal
State and Fuel Type Summaries From Plant Level Data -
Pre-Processing and Data Engineering
ETL Pipelines - PySpark
Building Energy Consumption Prediction
This is a comprehensive report that explores and transforms a set of survey questions about commercial building characteristics, extracts the most impactful features pertaining to electrical and natural gas consumption, and uses those features to create a deep feed-forward neural network prediction algorithm for each fuel source.
Full Report Download
Summary Download
New York City Energy Consumption Visualized
This project was created in an effort to showcase some programming and visualization skills I acquired. It is built using HTML, CSS, Javascript (d3.js), and the Plotly graphing package. It is a little slow, as it is hosted on a free platform, but it serves the purpose of showing what can be visualized from such data.