02 — HDD Regression & Normalization
Runs OLS regression per postal code, applies quality filters, normalizes slopes by MPAC building stock characteristics, and produces a thermal intensity metric for neighbourhood ranking.
regression normalization hdd building-stock
Pythonpandasnumpyscipymatplotlibseaborn
Key Findings
- ● OLS regression per postal code achieves R² > 0.80 for the majority of residential postal codes
- ● Normalization by heated volume materially reshuffles the raw slope ranking
- ● Normalized thermal intensity correlates strongly with ground truth in the synthetic validation