| Month | Load (BTU/hr) | Capacity (BTU/hr) | Aux share of hours | Aux energy (MMBTU) |
|---|
Why the balance point defines comfort and electrification success
The balance point of a heat pump is the outdoor temperature at which the machine can just barely keep up with a building’s heating losses without help from resistance coils or a fossil-fuel backup. When the mercury sinks below that threshold, auxiliary heat systems spring into action, driving up electric demand and operating costs. Homeowners considering gas-to-electric conversions often wonder how much auxiliary heat they will need, yet most manufacturer literature only lists rated capacities at two test points. This calculator turns the static data into a dynamic seasonal forecast, equipping energy auditors, contractors, and motivated homeowners with the context they need to match equipment to climate.
Because buildings are diverse, the inputs let you describe your envelope and heat pump rather than relying on default climate tables. The UA value captures how many BTUs per hour leak out for every degree of temperature difference between inside and outside. Internal gains account for the steady trickle of heat from appliances, lighting, and occupants that reduces the load the heat pump must supply. Capacities at 47°F and 17°F mirror the Air-Conditioning, Heating, and Refrigeration Institute (AHRI) test points that manufacturers are required to publish. With those pieces, the script interpolates capacity for every monthly average temperature you provide, revealing how closely the compressor’s curve tracks the building’s demand profile.
Formulas behind the seasonal model
At its core, the calculator compares two temperature-dependent lines. The building load line slopes upward as the outdoor temperature drops, because more heat leaks out. The heat pump capacity line slopes downward in cold weather for air-source equipment. The balance point sits where these lines cross. The building load is modeled as:
, where
The heat pump’s available capacity is interpolated linearly between the rated points: C(T) = C47 + slope × (T − 47°F), with slope derived from the difference between the 17°F and 47°F ratings. When the load exceeds capacity, the shortfall is satisfied by auxiliary heaters. The auxiliary share of hours in a given month equals the shortfall divided by the total load, assuming the thermostat cycles the backup heat only during the fraction of time when the compressor alone cannot hold setpoint. Energy shortfall is calculated by multiplying the shortfall rate (BTU/hr) by the number of heating hours provided for the month.
Worked example: auditing a 1960s ranch retrofit
Imagine a 1,600-square-foot ranch house in Columbus, Ohio. A blower door test indicates an overall UA of 420 BTU/hr·°F after air sealing and insulation upgrades. The homeowners maintain a 70°F setpoint and report consistent internal gains around 3,500 BTU/hr from lighting, cooking, and electronics. They are considering a 3-ton cold-climate heat pump rated for 38,000 BTU/hr at 47°F and 24,000 BTU/hr at 17°F. They compile historical heating-degree data that translate into average outdoor temperatures and estimated heating hours for each month, which you can edit in the form above. After entering the numbers and submitting the form, the calculator reports a balance point around 31°F. That means whenever the weather is warmer than freezing, the heat pump alone should suffice.
The monthly table goes further. In this scenario, November, December, January, and February show auxiliary shares ranging from 8% to 42% of heating hours because those months fall below the balance point. January’s average 25°F temperature produces a load of about 18,900 BTU/hr against a capacity of roughly 26,800 BTU/hr, yielding only a modest shortfall. February, with an average of 22°F, reveals a greater gap and predicts approximately 62 auxiliary hours over the month. Summed across the season, the results indicate 185 auxiliary hours and 3.1 MMBTU of backup energy—information the homeowner can use to estimate electricity costs or decide whether to retain a fossil furnace for deep freezes.
Comparing envelope and equipment strategies
Balance point planning becomes a practical decision tool when comparing improvements. The table below summarizes how common retrofit choices influence the UA value, the heat pump curve, and the resulting auxiliary usage.
| Strategy | Typical effect on inputs | Resulting change in aux hours |
|---|---|---|
| Air sealing and insulation | Lowers UA by 10–40%, decreasing the slope of the load line. | Reduces auxiliary runtime proportionally, often by hundreds of hours per season. |
| Upgrading to a variable-speed cold-climate heat pump | Raises 17°F capacity relative to 47°F, flattening the capacity decline. | Shifts the balance point downward and cuts auxiliary energy dramatically. |
| Adding smart controls with outdoor lockout | Leaves UA and capacity unchanged but delays auxiliary staging. | Reduces auxiliary share of hours by trimming unnecessary backup calls. |
Running the calculator with revised UA values or capacities makes these changes tangible. For example, if weatherization drops the UA from 420 to 360 BTU/hr·°F, the balance point shifts to 27°F and auxiliary hours fall by a third. Alternatively, choosing a larger compressor with 30,000 BTU/hr at 17°F compresses the auxiliary season to only the coldest weeks of winter. Seeing those dynamics spelled out encourages better coordination between building envelope contractors and HVAC installers.
Interpreting the outputs for retrofit decisions
The result summary consolidates the hours and energy that auxiliary heat will consume. Those numbers are meant to spark planning discussions. If auxiliary energy is concentrated in one or two months, you might decide to leave a fossil furnace in place as an emergency backup while still covering the majority of the heating season with the heat pump. Alternatively, you could accept the auxiliary runtime but pair it with a time-of-use rate analysis to understand cost exposure during peak pricing windows. Because the calculator reports the balance point in degrees Fahrenheit, it is easy to compare it against historical weather records to see how many days typically fall below the threshold, turning an abstract calculation into a concrete expectation.
You can also use the data to justify envelope upgrades to skeptical stakeholders. Showing that every 10% reduction in UA trims dozens of auxiliary hours helps homeowners weigh the value of weatherization incentives. When working with multifamily buildings, the same logic highlights which units might need supplemental radiators or baseboard heaters to avoid tenant complaints. Designers can document the expected balance point in project specifications, ensuring that installers set up outdoor lockout temperatures or staging thresholds appropriately.
Commercial facilities teams can go further by incorporating the CSV download into energy models. Import the monthly shortfall into a spreadsheet, convert the BTU values to kilowatt-hours, and overlay local demand charges. This approach is especially useful for schools and houses of worship, where occupancy spikes on certain days can coincide with cold snaps. By simulating how auxiliary heaters might stack onto existing electrical infrastructure, you can decide whether panel upgrades are necessary before replacing a boiler plant.
Making the most of the CSV export
The download button generates the same monthly table you see on screen, which is ideal for scenario comparisons. Analysts often test different heat pump models by tweaking the 17°F capacity rating. Others experiment with tighter thermostat setbacks or different indoor setpoints to see how occupant behavior alters load. Once you have multiple CSV files, you can combine them in a spreadsheet to build charts showing how auxiliary hours shrink as capacity improves. That visualization resonates with clients who have difficulty interpreting tables or percentages.
The CSV also opens the door to combined heating and cooling planning. Although this tool focuses on heating, the structure mirrors what you would need for cooling season analysis. Energy consultants sometimes calculate the total annual compressor runtime by adding summer and winter hours, then assess whether a given heat pump model meets manufacturer limits. Because the CSV lists load and capacity separately, it is easy to merge it with cooling calculations to get year-round totals.
Utilities and community choice aggregators can embed the exported data into rebate workflows. For example, a program could require contractors to submit before-and-after auxiliary projections as part of a quality assurance check. The CSV provides auditable numbers that align with the narrative in the explanation, reinforcing consistent assumptions across the market.
Limitations and assumptions to keep in mind
The tool intentionally streamlines several phenomena. Real heat pumps do not decline perfectly linearly with temperature, particularly near defrost cycles or when crankcase heaters engage. The calculator’s linear interpolation is a reasonable approximation in the mid-range but may misstate output during extreme cold snaps. Likewise, monthly average temperatures mask short spikes that could trigger auxiliary heat briefly even if the average sits above the balance point. For more granular accuracy, you could swap the monthly inputs for weekly or even daily bins.
Thermostat logic also varies across models. Some systems stage electric resistance heat based on time rather than true load shortfall, which can inflate auxiliary use beyond what the thermodynamic balance suggests. Conversely, sophisticated controls can squeeze extra performance out of the compressor before calling for backup. The script assumes the heat pump always contributes its full interpolated capacity before the auxiliary stage turns on. Finally, the UA value is treated as constant, though infiltration and radiation often change with wind speed and solar gains. Treat the outputs as directional planning insights rather than precise predictors, and consider pairing them with load calculations or manufacturer performance tables when finalizing equipment selections.