Inspiration
Six million New York buildings account for ⅓ of statewide greenhouse gas emissions (GE). Successful building energy conservation measures are crucial to lower GE. Such measures are costly to implement. Energy star score is an existing efficiency metric but may inadequately describe building-level performance. It is crucial to accurately spot energy inefficiency at a local building level in a time- and cost-economical manner. We provide a framework for practitioners to easily identify these targets.
What it does
Spotting low energy-efficient buildings across NYC and providing policy recommendations.
How we built it
Data Cleaning. Bayesian Modeling, Data Visualization
Accomplishments that we're proud of
Incentivize energy-efficient improvements to tenements, especially in older properties & affordable housing. Investigate disparity in energy cost burden through additional demographic data. Prioritize clean-energy implementation plans that allow for building-level use of green power. Re-evaluate Energy Star scoring in a more holistic manner.
What's next for NYC Greenhouse Gas Emission - Where and Why?
Quality: a more systematic Bayesian approach may be taken for missing data. Spatio-Temporal Modeling: exploit spatial correlation and temporal variations.
Built With
- colab
- pymc
- python
- visualization
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