Switched gas furnace → heat pump (July 2024): ~18% lower energy bill at the same outdoor temp [OC] Visualization

What This Visualization Shows

This data visualization displays "Switched gas furnace → heat pump (July 2024): ~18% lower energy bill at the same outdoor temp [OC]" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on Converted our house from a gas furnace to a heat pump in July 2024 (Canada). We still have a gas stove, and we gained central AC for the first time as part of the install.

To compare apples-to-apples, I plotted total monthly energy cost (gas + electricity) against average daily outdoor temperature, and restricted both series to ≤10°C so the comparison stays within heating season for both — no AC cost muddying the "after" data.

**Before (gas furnace):** y = -8.08x + 230.16 **After (heat pump):** y = -5.60x + 188.54

At 0°C, that's **$230/month before vs. $189/month after — about 18% lower**, with the same gas stove cost baked into both, so the difference is attributable to the heating source itself.

The slopes are the other interesting bit: the heat pump line is noticeably flatter (-5.60 vs. -8.08), meaning our costs are less sensitive to how cold it gets outside. The gas furnace had to work (and cost) proportionally more as temps dropped; the heat pump's cost curve is comparatively steadier.

A few notes for the data purists:

* Data pulled directly from utility bills (Enbridge gas + electricity), anomalies normalized (estimated gas usage vs actual reading restatements), matched to average daily temp for each billing period. * Restricted to ≤10°C specifically to exclude AC-driven costs, since AC is new post-heat-pump and would confound a straight before/after comparison at warmer temps. * Sample size is modest (roughly 8-10 points per series), but each point represents a full billing cycle (\~30 days) of actual metered usage, not a spot reading — so while there are few points, each one is already an aggregate/average over a month, not a noisy single-day sample., which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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