Add Building Grades Data

[AlexanderTyan]

Description

This generates numerical and letter grades for each building and each year based on GHG Intensity, Energy Mix, and Reporting Status. The results are under src/data/dist/building-benchmarks-graded.csv

Additionally, this PR contributes the research notebook with initial EDA and visualization around grading (which also has some further ideas to explore). All relevant research notebook files are under src/data/research

Realtes to #129 (but only the data processing part; still needs to be integrated into UI)

Example Grades

According to the grading so far in this PR, these are a few selected grades (overall across three grading categories):

• "Crown Hall": 18.1784/100 or F
• "Keating Hall": 5.1458/100 or F
• "Merchandise Mart": 64.4056/100 or B
• "Marina Towers Condominium Association": 64.5743/100 or B

Visual Results

Overall Numerical Grade Distribution

Overall Letter Grade Distribution

Testing Instructions

Building grading is integrated into the docker compose-based script and is consistent with the instructions in the repository README.me. I.e. docker-compose run --rm electrify-chicago bash run_all.sh produces the graded file into the src/data/dist/building-benchmarks-graded.csv (via functions in src/data/scripts/grade_buildings.py). building-benchmarks-graded.csv is the same as building-benchmarks.csv but with grading columns attached. Specifically, these are the columns added:

• GHGIntensityPercentileGrade: The building this year is better than this percent of other buildings within that same year in GHGIntensity.
• GHGIntensityLetterGrade: Letter grade based on GHGIntensityPercentileGrade.
• EnergyMixWeightedPctSum: The weighted sum of percent of energy consumed by this building from different energy sources reported.
• EnergyMixWeightedPctSumPercentileGrade: This building is better than this percent of other buildings within that same year in EnergyMixWeightedPctSum.
• EnergyMixWeightedPctSumLetterGrade: Letter grade based on EnergyMixWeightedPctSumPercentileGrade.
• MissingRecordsCount: How many years this building is missing. This value is the same for the same building across all years.
• MissingRecordsCountPercentileGrade: The building is better than this percent of other buildings in MissingRecordsCount.
• SubmittedRecordsGrade: Letter grade based on EnergyMixWeightedPctSumPercentileGrade.
• AvgPercentileGrade: Average of all percentile grades for this building for this year.
• AvgPercentileLetterGrade: Letter grade based on AvgPercentileGrade.

The research notebook dev environment is reproducible via docker compose by following instructions in the README.md under src/data/research. The notebook is also included at src/data/research/building_grade_eda.ipynb.

Checklist:

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes

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[AlexanderTyan]

These are the A-graded buildings:
a_grade_buildings.csv

IDs (from 2022):

117024
124236
160142
165717
172137
251770
254001

[AlexanderTyan]

TODO: building-benchmarks-graded.csv should just be incorporated into building-benchmarks.csv

[vkoves]

@AlexanderTyan - I wanted to check in on this - I'm going to start trying to look at detecting anomolous buildings, so if it's helpful maybe we can get this into a mergable state where we have the grades integrated into the main data and can console.log them from each building details page. Then we can start rendering them once we have safe-guards in place against those bad data buildings

[AlexanderTyan]

@AlexanderTyan - I wanted to check in on this - I'm going to start trying to look at detecting anomolous buildings, so if it's helpful maybe we can get this into a mergable state where we have the grades integrated into the main data and can console.log them from each building details page. Then we can start rendering them once we have safe-guards in place against those bad data buildings

@vkoves ah sorry about that. yeah, let me work on that this week