Coincidence Factor Calculator

Professional notes on coincidence factor

Coincidence factor (CF) is the ratio of a group’s maximum simultaneous demand to the sum of each item’s maximum demand. It supports realistic sizing of feeders, panels, transformers, and standby capacity. In practice, CF helps avoid oversizing while preserving sensible safety margins.

1) What the number tells you

A CF near 1.00 suggests loads peak together with little diversification. A lower CF indicates staggered operation, occupant variability, or duty cycling. Designers use CF to translate item peaks into a single diversified group peak for upstream selection.

2) Typical early-stage ranges

Concept design often starts with broad planning ranges: residential unit groups 0.35–0.70, offices 0.50–0.80, and retail 0.60–0.90 depending on hours and tenant mix. Treat these as placeholders until schedules and controls are confirmed.

3) Separate base loads from intermittent loads

Continuous or essential loads (life-safety, critical ventilation, IT rooms) behave closer to full coincidence. Intermittent loads (plug loads, kitchen equipment, pumps) diversify more. Categorizing the list improves review clarity and avoids missing base demand or double-counting.

4) Coincidence vs. demand vs. diversity

Demand factor compares a system’s maximum demand to its connected load. Diversity factor is the inverse of coincidence factor: sum of individual maxima divided by group maximum. This calculator focuses on CF for grouped peaks when aggregating many spaces and panels.

5) Motors, elevators, and short spikes

CF describes coincident steady peak demand, not instantaneous inrush. Large motors and elevators can create brief spikes that affect coordination and voltage drop. Pair CF-based sizing with starting data, duty cycles, and control sequences to prevent nuisance trips.

6) Use metering and trends when possible

The strongest CF inputs come from interval demand data (often 15-minute) from similar facilities or on-site temporary metering. Identify the coincident peak window, then document season, occupancy, and operating mode. This makes the CF defensible during reviews and value-engineering discussions.

7) Schedules and phasing change coincidence

Staggered tenant hours lower coincidence, while synchronized start times raise it. Construction phases can temporarily increase CF as temporary loads cluster. Model scenarios like weekday peak, weekend events, and emergency mode, then choose margins intentionally.

8) Final checks before locking equipment

Validate diversified demand against utility limits, transformer kVA, feeder ampacity, and panel capacity. Add allowances for EV charging, tenant churn, and future fit-outs. If CF is unusually low (below ~0.30), recheck that realistic maxima and base loads are included.

FAQs

1) What is the difference between coincidence and diversity?

Coincidence factor is group maximum demand divided by the sum of individual maximum demands. Diversity factor is the inverse: the sum of individual maxima divided by the group maximum. They describe the same relationship, just flipped.

2) Can coincidence factor be greater than 1.00?

Normally, no. If you mix different definitions of maximum or use non-peak values for some items, the calculated CF can exceed 1.00. Review the time basis, units, and whether each item maximum is truly a maximum.

3) Should I apply coincidence to emergency and life-safety loads?

Be cautious. Emergency scenarios often assume many required loads operate together, so coincidence may be close to 1.00. Follow code requirements and the authority’s expectations, and document the emergency operating sequence explicitly.

4) What demand interval should I assume for maximum demand?

For service sizing, align with the utility billing interval, commonly 15 minutes. For internal studies, shorter intervals can capture brief peaks. Always state the interval used so results remain comparable across projects.

5) How do I estimate coincidence factor without metering?

Use documented planning ranges from similar buildings, then adjust for schedules, tenant mix, and control strategies. Apply conservative margins and update the CF when equipment selections and operating hours become clear.

6) How do plug loads affect coincidence?

Plug loads diversify strongly because occupants rarely use everything at once. However, modern office peaks can rise with dense IT equipment and charging. Separate workstation loads from dedicated equipment loads, and validate with submeter data when possible.

7) What should I do if my CF seems unrealistically low?

Recheck base loads, confirm each item maximum is reasonable, and look for duplicated diversity assumptions. Very low CF values can indicate missing continuous loads or using averaged demand instead of maxima. Add scenarios and compare against similar projects.