Turning Kilowatt-Hours into Cents: The Logic Behind Gwenya's Billing

While piloting Gwenya, my AI-driven smart energy adapter, a potential early adopter pointed at a problem I hadn’t prioritised: shared retail spaces. Several small shops on one meter, endless arguments about who owes what.

The fix wasn’t more hardware. It was honest maths, made visible.

From energy to money

The adapter’s energy-monitoring module reports consumption in kilowatt-hours. Turning that into a fair bill is straightforward once you have per-appliance data:

def cost_breakdown(readings_kwh: dict[str, float],
                   tariff_per_kwh: dict[str, float]) -> dict[str, dict]:
    """Translate per-appliance energy use into ZiG and USD."""
    breakdown = {}
    for appliance, kwh in readings_kwh.items():
        zig = round(kwh * tariff_per_kwh["ZiG"], 2)
        usd = round(kwh * tariff_per_kwh["USD"], 2)
        breakdown[appliance] = {"kwh": kwh, "ZiG": zig, "USD": usd}
    return breakdown


tariffs = {"ZiG": 3.85, "USD": 0.14}
usage = {"Fridge": 4.2, "Lights": 1.1, "POS Terminal": 0.6}

for name, cost in cost_breakdown(usage, tariffs).items():
    print(f"{name:14} {cost['kwh']:>5} kWh  ZiG {cost['ZiG']:>6}  USD {cost['USD']:>5}")

Why transparency won people over

Nobody argues with numbers they can see. By giving each user individual, transparent consumption data — down to the cent — Gwenya replaced guesswork with trust. That single feature did more for adoption than any of the flashier ones like voice control or scheduling.

Real-world impact often looks like this: not a breakthrough algorithm, but a small piece of clarity dropped into a place where people were quietly frustrated.