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Great question! There is not a way to directly specify this. It’s not really important information for the energy model, but it does mean that some of the area estimations made by OptiMiser will be incorrect. You will need to perform your own calculation for Wall Area and specify the foundations us using Area and Perimeter, rather than Length and Width. The default distribution of wall area across the four facings of the house will also be off, but this very little impact, unless you are making wall or window improvements. The wall distribution can be set in the Net Areas popup, accessed on the Walls tab.
EDIT: Wood is now available as a Fuel selection for the heating systems.
The cooling system can be specified normally. Selecting a heat pump in the Cooling System control does not automatically apply the heat pump to the heating load. You have to specify it in the Heating System control to make that happen.
This post covers how to model wood heat: https://optimiserenergy.com/forums/topic/how-do-i-model-a-ducted-wood-burning-furnace
There are a few ways that you could work around this, depending on the setup.
If there is no crawl space, then the Crawl Space control set can be used to as a very close approximation for the floor over the garage. Be sure to specify it as an Unconditioned Crawl and if you want to get even closer, you can open the More popup and set the % Tempered by Indoor control to 35% or whatever setting you feel is best for the garage.
If that is not an option, but the two floors are identically insulated and will be improved in the same way, then you can lump them together in one area and adjust the % Temper control to reflect a weighted average of the two spaces (e.g. % Temper = 35*GarageArea/TotalFrameFloorArea).
If neither of these solutions will work for you, then please give me a little more detail about the condition of the floor and your planned improvements.
There are two locations to specify a zip code in OptiMiser. On the Owner/Project tab you can specify the zip code of the owners address. There is also a separate building zip on the Owner/Building tab in case the home is not owner occupied. This owner zip code is automatically used to pre-set the building zip, but the weather data an other regional data are not loaded until the building zip code is set manually or confirmed by clicking the Load button to the left.
If you are getting this message, it is an indication that weather and other regional data has not been loaded. Click the Load button to make the message go away. If you are certain that you have already loaded that data, but OptiMiser is still giving you that message, then please submit an error report.
Unfortunately we don’t currently have the means to directly model the impact of attached garages (with of the floor of a conditioned space above a garage). It is relatively high on our list of future improvements.
The walls and doors of the garage itself (i.e. the boundaries between the inside of the garage and the outdoors) should never be modeled in OptiMiser, unless the garage is directly conditioned. In this case, it should be treated as part of the home.
The walls and doors of the main home that are adjacent to the garage will effectively have a lower U-factor. The impact would vary significantly with climate and configuration, but I think 30% reduction would be reasonable. For a door this would have negligible impact on overall building loads, but for an uninsulated wall it could be significant. You can view the U-factor for the wall on the ||Path popup. For a typical frame wall it would be about equivalent to adding an R-2 batt.
The utility analysis overrides should only be used in the following circumstances:
- The utility analysis doesn’t pass BPI criteria, but statistical fit measures are within 20% and the results are in a normal range. In this case you can override the BPI rejection and select Yes.
- The utility analysis passes BPI criteria, but OpitiMiser rejects the analysis because either the BLC/Eff or Base Temperature represent a home that would be very unusual. In this case you should only override the rejection, if the accompanying description on the Analysis Details and Overrides popup matches your knowledge of the home (e.g. exceptionally efficient envelope/systems or very low thermostat setting).
- The utility anslysis passes BPI, but OptiMiser detects unusual results. In this case OptiMiser has conditionally accepted the anlaysis and you can force a rejection by selecting No. You should do this if the accompanying description is contrary to your knowledge of the home.
If you any of these cases if your final decision is to accept the analysis, you should be on the lookout for unusual savings results. If you see results that don’t make sense, then you should try rejecting the analysis and see if the results look better.
If the analysis message indicates that the bills did not provide a good signal, then you will not be able to override the acceptance of the results. A majority of your bills should pass the BPI criteria, provided that you enter actual bill dates and properly mark estimated bills or known vacation months. If this is not the case, please send us a selection of files that fail, along with any bill documentation that you have available.
It turned out that the OP had made an inappropriate override of the rejection of the cooling utility analysis. OptiMiser rejects the analysis when the results describe a home that has unusual properties. You should only override the rejection, if the home is unusual in the way described on the Analysis Details and Overrides popup. Overriding the rejection, when the home does not match that description can cause the model to stretch too far in it’s attempt to self-calibrate and deliver unstable results.
My understanding is the technology will improve the efficiency of any electric induction motor. The potential improvement for each motor depends on how the motor is sized relative to the it’s working loads and I think it varies significantly.
We do not currently have a way to store lifetimes for individual units, but we hope to add that sometime in the future. When we do, it will be edited through the screen pictured in your attachment.
Currently you can only edit the general lifetime for the Upgrade DHW improvement, as described in my initial reply. I have attached a screen shot to illustrate. The Life Time column appears as the third from the left, when you have checked the “O and M” box.
We don’t offer direct support for this technology. You could model it by manually improving the efficiency specs of the affected units, but you would need a rough estimate of the impact on each unit. I have only seen estimates of whole house savings. If you can get those estimates, they would be easy to apply for most units.
- EAE – for furnace fans and boiler pumps
- HSPF – for heat pumps heating
- SEER – for cooling
- kWh/yr – for refrigerators and freezers
- % Use – for washer, dryer, and dishwasher
- watts – ventilation fans
If the KVAR unit is already installed, then apply the reduction to all base units and to any improved units that will be part of a considered improvement package.
If you are trying to model the KVAR as an improvement, then follow these steps
- Specify all other improvements normally
- Apply KVAR adjustment to each improved unit
- Specify an improvement for all other units that is identical to the base unit, with the exception of the KVAR adjustment.
- Be sure to override the cost of those “base” improvements and set them to zero
- Either apply the cost of the KVAR itself to an additional row on the Improvements/Costs table or distribute it among all affected units.
I’ve added this to our list of future projects, but it will remain low priority unless we begin to receive more requests. Yours was the first.
- The utility analysis doesn’t pass BPI criteria, but statistical fit measures are within 20% and the results are in a normal range. In this case you can override the BPI rejection and select Yes.
