Appliance Energy Audit

Overview

Your utility bill says you used 850 kWh last month — but says nothing about which device used them. This skill turns a walk-through inventory of your home ("fridge from 2012, gaming PC, window AC…") into a ranked cost table: what each appliance consumes per month and year, what share of the bill it represents, how much pure standby ("vampire") draw is costing, and — when you feed in your actual bill kWh — whether the model matches reality or something is running more than you think.

It also answers the eternal appliance-store question: is the new efficient model actually worth it? The replace command computes monthly/yearly savings and payback months for any old-vs-new pair at your electricity rate.

When to Use

• "Which appliances are costing me the most?" / "Why is my electric bill so high?"
• "Is it worth replacing my 15-year-old fridge / electric dryer / AC?"
• "How much do all these always-on devices (router, console, smart speakers) cost me?"
• "My bill jumped — does my usage actually explain it?"
• "I have tiered rates — how does that change the picture?"
• Don't use for: gas appliance costs, whole-house heat-loss modeling, or real-time monitoring (this is an analytical model, not a meter).

The Model

Each appliance is characterized by four numbers (all have sensible library defaults):

monthly_kWh = (watts × hours_day × 30 × duty  +  standby_w × (24 − hours_day) × 30) × qty ÷ 1000
cost        = kWh × rate        (flat)
            = tiered_cost(kWh)  (tiered rate plans)

Vampire draw is the standby term summed across appliances. Full derivation and the tiered-rate marginal-cost approximation are in references/energy-model.md.

Commands

# Browse the 44-appliance preset library (watts, duty, default hours, standby)
python3 scripts/energy_audit.py library

# Quick single-appliance estimate
python3 scripts/energy_audit.py estimate "my dryer,electric-dryer,0.5" --rate 0.25

# Full audit — repeat -a for each appliance, ranked by monthly cost
python3 scripts/energy_audit.py audit -a "fridge,fridge" -a "tv,tv-oled-55,5" \
    -a "bulbs,led-bulb,4,8" -a "gaming pc,gaming-pc,3" --rate 0.17

# From a JSON inventory file, with tiered rates and bill reconciliation
python3 scripts/energy_audit.py audit -f home.json --tiers "0.12:500,0.15:1000,0.20:" --calibrate-to 850

# Model vs actual bill only
python3 scripts/energy_audit.py calibrate -f home.json --bill-kwh 620

# Replacement math: old fridge vs $800 efficient model
python3 scripts/energy_audit.py replace --old "old fridge,fridge" \
    --new "efficient fridge,120,0.3,24" --price 800 --rate 0.17

# See everything at once on sample data
python3 scripts/energy_audit.py example

Appliance shorthand: name[,preset|watts][,hours_day][,qty][,standby_w] — with a preset, numbers mean hours/qty/standby; without one, the first number is watts. JSON spec files are lists of the same fields in long form ({"name": ..., "preset": ...} or {"name": ..., "watts": ...}).

Workflow

1. Inventory your home: walk room to room; for each device run library to find its preset or read the nameplate watts.
2. First pass: audit with defaults — the preset library already encodes realistic duty cycles.
3. Calibrate: get your bill's monthly kWh, run with --calibrate-to. If the model says "undercounted", your heating/cooling or an aging compressor is running harder than rated — raise those hours_day/duty values and re-run.
4. Act: attack the top of the table. Use replace on any aging big-ticket row to get payback months; put high-standby devices (flagged automatically) on switched power strips.
5. Re-check quarterly or after any bill spike — the diff between runs is the story.

Common Pitfalls

1. Forgetting duty cycle for compressor appliances. A fridge is "on" 24 h/day but its compressor runs ~35% of the time. Using duty 1.0 triples the estimate. The presets encode this — prefer them over nameplate watts.
2. Using nameplate watts as typical draw. Ratings are peak/startup, not average. A desktop PSU rated 600 W averages ~200 W. Use measured or library values; nameplate only as a last resort.
3. Ignoring tiered rates when you have them. On tiered plans the next kWh can cost 60% more than the first — flattening to one rate distorts which appliance matters. Pass --tiers.
4. Trusting the model over the bill. If calibrate shows a big gap, the model is wrong somewhere — not your meter. Find the missing/underestimated device (almost always HVAC run-time or water heating) before making purchase decisions.
5. Double-counting qty. -a "bulbs,led-bulb,4,8" means 8 bulbs at 4 h/day. If you also list bulbs individually you'll count them twice.

Verification Checklist

• python3 scripts/test_energy_audit.py → ALL TESTS PASSED (63 assertions)
• python3 scripts/energy_audit.py library prints the preset table
• python3 scripts/energy_audit.py example renders a ranked audit + calibration + replace verdict
• audit --calibrate-to <bill kWh> gap is under ±10% before trusting per-appliance numbers