Calculation Methods

How we build your demand profile.

Calculation Methods Summary

Base Load Profile

The tool starts with a standard residential hourly load pattern that varies by day type:

  • Weekday pattern: Lower overnight (0.35–0.4× average), morning rise (0.5–0.8×), moderate daytime (0.55–0.7×), evening peak (1.0–1.1× around 6–9 PM)
  • Weekend pattern: Similar shape but higher daytime usage (0.7–1.15×) and a more gradual evening peak

The base hourly kW is calculated as:

Base kW (hour) = (Annual kWh ÷ 365 days ÷ 24 hours) × Hourly Load Factor

Climate and Location Influence

Your zip code maps to a climate zone (1–5) that estimates:

  • Cooling Degree Days (CDD): Higher in warmer regions, used to scale summer AC load
  • Heating Degree Days (HDD): Higher in colder regions, used to scale winter heating load

Seasonal Adjustments

Air Conditioning (Summer Only)

  • Peak cooling hours (2–6 PM): Additional load = Base kW × (1.0 + (CDD ÷ 2000) × 0.5)
  • Extended hot hours (12–8 PM): Additional load = Base kW × (1.0 + (CDD ÷ 2000) × 0.3) × 0.5

Electric Heating (Winter Only)

  • Peak heating hours (5–8 AM, 6–10 PM): Additional load = Base kW × (1.0 + (HDD ÷ 2000) × 0.6)
  • Overnight heating (12–6 AM): Additional load = Base kW × (1.0 + (HDD ÷ 2000) × 0.4) × 0.7

Work-From-Home Adjustments

If work-from-home is selected:

  • Weekdays, 9 AM–5 PM: Base load × 1.3 (30% increase)
  • Accounts for computers, lighting, and additional HVAC during work hours

Electric Vehicle Charging

If EV charging is selected:

  • Evening charging (6–10 PM, weekdays): 60% probability of adding 7 kW
  • Overnight charging (10 PM–6 AM): 80% probability of adding 7 kW
  • Represents typical Level 2 charging patterns

Final Normalization

After applying all adjustments, the profile is normalized to match your annual kWh:

Normalization Factor = Your Annual kWh ÷ Calculated Total kWh
Final kW (each interval) = Adjusted kW × Normalization Factor

This ensures the total annual energy matches your input while preserving the relative demand patterns.

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