TEUI G.4.1 Net Usable Gains

First of all, if you’re not sure what TEUI3 is, Take me to the Index of Articles!

If there is one, single thing that differentiates Passive House Energy Modelling from most other methods, including Hourly, Dynamic modellers, it is probably what is called the Net Usable Gains Calculation. Just semantically, think of a building as an empty vessel where nobody is home, and then we have the first hour of occupancy. Energy is added by the occupants themselves (we all emit sensible and latent heat/energy) and then we start switching on the lights, equipment starts to run, we draw the blinds and let the sun shine in, we turn on equipment and lights and cook and run hot water and use computers, etc. Some of these additions of energy into the space of the building can be considered Passive (Solar Gains through windows) and some are Active such as the additional of heat from a furnace. Passive House is all about retaining the passive gains better, so that the building is generally way less dependent on thermal energy from a furnace. Good approach. We can think of a building like a good thermos, keeping the heat instead of losing it through transmission through poorly insulated envelope components and leaky construction. But even when insulation is improved, and airtightness is increased, even in the very best Passive House – there is transmission loss through components and a smaller amount of air leakage, it is just reduced a substantial amount, anywhere from 70% to 90% of conventional construction. We need more airtight buildings, without question!

So, then how can we classify these passive and active gains, and then quantify them numerically, and then lastly determine how much of these stay inside the building versus being transmitted through the envelope, or out through ventilation exhaust ducts, or open windows, or through air-leakage? PHPP uses a shorthand for most of these Tranmission Losses and Gains represented by the letter Q.

1. Q_S = Solar Gains
2. Q_I = Internal Gains
3. Q_T = Transmission losses (losses through things like walls and roofs and floors, etc)
4. Q_V = Ventilation system Losses
5. Q_L = the Sum of all Losses
6. Q_G = the Sum of all Gains

It’s not just PHPP that uses these terms, but the global energy modelling community uses similar terms. OBJECTIVE changes this up just a little bit to help us see where the gains and losses belong, where the prefix G is used for all Gains and B is used for all Building related transmission losses, and then we have V for ventilation losses and M for Mechanical Energy Supplied, etc.

So both systems have names for the Energy Supplied to the Building and Energy Lost from the building. This is fundamental to the ‘Energy Balance’ methods we will describe later, but as with Gravity’s ‘What Goes Up, Must Come Down’, so in energy modelling we have ‘What Goes In, Must Come Out’ – and this deals with the nature of dynamic energy scenarios and thermodynamic flow from higher energy states to lower, and where nevertheless a steady-state interior temperature is desired, a function of our Tset or Thermostat Setpoint, ie. 22ºC or 18ºC or whatever you prefer – buildings need to account for, and manage gains and losses and provide additional active thermal energy when the gains fall short of heating the space on their own.

So where PHPP and Passive House was developed, In Germany, buildings are generally made of much more robust materials than in North America. I think it is safe to say that if you put a typical 2,000sf Canadian stick-framed house on a scale and a typical 2,000sf German House (take your pick, masonry construction or timber), you would probably find the German house would bottom out the scale. They might even be double the mass. German engineers I have worked with actually laughed at our proposed framing for my first passive project near Stuttgart, they called it ‘toothpick framing’ and ‘matchstick framing’. We build in Canada and the USA for economy, not for durability, where in Europe, considering a home as a 500yr, multi-generational asset is not uncommon.

So what does the typical mass of a German house have to do with Net Usable Gains? It turns out, a lot. The more mass a building has, the more capacity it has to absorb thermal energy generated in its interior from any source, whether that be occupant gains, or solar energy transmitted through windows onto adjacent floors or walls, or from heating systems warming interior surfaces or air and eventually penetrating into the depth of the materials where it is kindof stored like a thermal battery.

The more mass, the more capacity to transfer energy and store it. The ability for a building to receive and store this energy can be modelled, and that is done with an equation for net gains. You have the sum of all gains, and then how much of these you can use is calculated, and then the rest is exhausted or lost through the envelope or ventilation systems. In a static analysis program (which both OBJECTIVE and PHPP are), this must be approximated with such a calculation. In dynamic hourly modellers like WUFI PHPP or OpenStudio, this is modelled on an hourly time-step. The latter is more accurate generally because dynamic modellers consider the rate of absorption and release of thermal energy into the materials (called Capacitance or Transience, hence the software named TRYNSYS = Transient Analysis, as one of the first that did this), and they consider on an hour-by-hour basis if a gain is useful or not.

Example: Imagine a sunny afternoon in late October, which is part of the Heating Season in Canada. We receive tons of energy through our South and East and West-facing windows, which causes the interior to become uncomfortably warm. So we throw open the windows and dump the excess heat (sometimes referred to as Free Cooling). These gains however are logged in static modelling tools as beneficial, as if they can be applied when we need heat later that same day – at night for example – but we intentionally got rid of the gains out the window! We did not in fact charge the materials for later use. In fact, this happens anytime in the Heating Season when we receive gains and the Tset for Heating has already been exceeded. So we know intuitively that not all gains are useful – we have to dump them sometimes, or we may not be able to store them adequately over time, and time considerations is what differentiates dynamic modellers, they are not necessarily better, just different. PHPP bins gains on a monthly basis. OBJECTIVE applies gains on a total/annual basis, and dynamic modellers apply gains and capacitance every hour or even across smaller time steps. PHPP uses an equation to derive the ratio of useful vs. unusable gains like this:

h_G = (1 – ( Q_F / Q_L )^5 ) / (1 – ( Q_F / Q_L )^6 )

Where h_G results in a percentage, and Q_F = the free or available total passive gains, and Q_L = total envelope transmission losses (Q_T) plus Ventilation system losses Q_V. Percentages generally calculate out to over 70%, which suggests over 70% of gains are useful. We also need to keep in mind that Germany has a relatively temperate continental climate when compared to Canada’s often icy and highly dynamic Winter seasons. OBJECTIVE takes a similar approach in what we call a PHPP-like calculation, where we use the following equation:

nGainsFactor = (1-((G11 + G23 + G31)/(B131 + B195 + V24 + TEL))^5)/(1-((G11 + G23 + G31)/(B131 + B195 + V24 + TEL))^6)

1. G11 = Occupant Gains (Bodies)
2. G23 = The Sum of All Solar Gains Through Windows (and glass doors)
3. G31 = ALPE or Appliance, Lights, Plug-Loads and Equipment Gains (these produce heat inside the building)
4. B131 = An independent penalty factor we use to account for Thermal Bridges
5. B195 = Envelope Air Leakage
6. V24 = Net Ventilation System Losses (ie an 80% efficient ERV loses 20% of total energy)
7. TEL = The Sum of all surface-based Envelope Transmission Losses

How then can we understand what the ‘powering’ of this fundamental ratio is doing?

The PHPP’s utilization factor equation (h_G) is actually a correlation formula developed through detailed dynamic simulations and/or research field measurements in Germany. The ratio (G11 + G23 + G31)/(B131 + B195 + V24 + TEL) represents the balance between available gains and potential losses – kind of like a gains-to-losses ratio. We can think of it as “how much energy is coming in” compared to “how much could potentially be lost.”

The power functions (^5 and ^6) then transform this ratio into a utilization factor that accounts for:

1. Building thermal mass (related to the ability to store heat – remember German and Canadian buildings do NOT have similar masses)
2. Time lag between gains and when they’re needed (our sunny October afternoon gains can’t be used the same night if we dumped them)
3. The fact that not all gains occur when heat is needed (I can’t use a Gain from October to Heat my Home in January)

So (1 - ratio^5)/(1 - ratio^6) creates a curve that:

• When gains are low compared to losses (small ratio), most gains can be used (~100%) – more common in Germany
• When gains are high compared to losses (large ratio), a smaller percentage can be used – more common in Canada
• The steepness of this curve reflects how well the building can match gains to needs – related to mass and transfer rates

This is why in practice:

• High mass buildings can use more gains (they can store more thermal energy)
• Direct gains during heating hours are more useful than random gains – easier to model with dynamic engines
• Excess gains often can’t be used even if total gains match total losses*

*unless they can be stored – Thermal storage devices uses phase change materials (PCMs) that absorb or release heat as they melt and solidify. These materials are typically integrated with pipes or systems circulating fluids (like water or refrigerant) to transfer heat, allowing homes to store energy for later use.

Here’s the fundamental problem. In Canada we are using PHPP extensively to model Canadian homes, in a Canadian Climate. We use appropriate weather files and transmission rates, etc. but it is possible we need a modified Net Usable Gains Ratio.

The NRC found in 1982, (authors: Barakat, S. A.; Sander, D. M.) from actual field measurements that usable gains in Canadian stick-framed homes fell between a ratio of 40-60%, which is typically much lower than what PHPP models, in some cases by half. These test platforms were based on conventional building details, not Passive Houses, so they can be thought of as more ‘lossy’, but making a house ‘Passive’ does not necessarily mean adding mass. Typically it means adding more insulation and better airtightness, but increasing mass is not an explicit PHI requirement. Here’s the link to the NRC research. This is why OBJECTIVE offers a few methods in its equations, you get an option for 0% usable gains, which you can use in very lower or intermittent occupancies (think of a Cinema as an example, sudden big gains and then almost nothing all day long), then 40%, 50% and 60% options for buildings with increasing levels of thermal mass, and then finally a PHPP-like calculation option. While PHPP tends to estimate higher passive solar gains than OBJECTIVE, OBJECTIVE takes a slightly different approach by accounting for a wider range of free internal gains and their varying utility, especially in typologies with high occupancy or equipment loads. This difference, while significant, reflects the need for more nuanced modelling when applied to extreme climates like Canada’s. If you are targeting PHI’s TEDI metric of 15kWh/m2/yr, you will very likely only reach that goal with the PHPP method. If you select an option that more closely approximates the research-derived values from NRC, you will invariably have a much higher TEDI value. Here is what Katrin Klingenberg has to say about the differences:

Ultimately, the buildings we have modelled in both PHPP and in OBJECTIVE target and simulate very similar values to post-occupancy review of utility bills. But what is occurring in reality may be less clear. The Canadian climate is not continental Europe, and Canadian Buildings are not mass-y like German Buildings, and furthermore Canadians are generally much greater consumers of Appliances, kitchen gadgets, electronics, and much higher percapita users of energy and water. Have you ever compared the size of Canadian vs. European refrigerators? PHPP assumes Canadians use 25l/day of water, where our statistics show we use about 275l/day. Germans are generally a very energy-conserving bunch, Canadians, not so much. So is it reasonable to assume the same kinds of gains?

Generally PHPP use much higher passive solar gains than OBJECTIVE, but much lower free gains than OBJECTIVE (we account for 100% application of ALPE loads as heat gains), the two different approaches seem to have a netting effect. When a building is NOT modelled using PH-type details and methods, it is likely better to select from the lower nGainsFactor options outlined by NRC (40-60%) as a more conservative method, noting that the TEDI value will likely be much higher, and as a result, so will your proposed heating system’s contribution to match losses. All of this stuff is much easier to understand when you have a visual analysis of what the various options look like – which you can test with our dynamic Sankey modelling app:

In conclusion: It’s important to understand the science, and then see that there are different methods to target loads, losses and gains, some methods are appropriate for Passive House designs, and others can support conventional and code-minimum construction dynamics better. When you can compare with a few different methods, the often surprising values obtained from a review of utility bills, entered in to a tool like OBJECTIVE can be the real test of what is actually taking place over the course of a year, where we can run a few different Net Usable Gains ratios – and see for ourselves which is the best fit.