Can you guarantee that the completed building will match the performance predicted by the analysis in its day-to-day operations?
No we can't guarantee that the completed building will match the performance predicted by the analysis in its day-to-day operations. This is because there's a lot of uncertainty in the energy model and a lot of broad generalizations used to create a relatively accurate prediction of energy usage.
For instance, the weather files often used in energy models are actual weather data from a historical, representative year. Day-to-day weather will greatly affect things like lighting and HVAC in the building. So, even though the energy model may give us a good energy usage model for a typical year, it doesn't necessarily translate well to a specific day.
Another important uncertainty is human behavior. In buildings, energy usage is highly dependent on occupants' usage (after all the building is used by occupants). What habits and behaviors these occupants exhibit will greatly change the building's energy usage. Occupants might use the building 16 hours a day when we only account for 12 hours. Occupants might decide to use their clunky, old appliances or leave their desktop monitors on all night. These things are not entirely predictable, and also, to some extend, outside the designer's control.
When choosing settings for each of the building performance factors, should you always choose the setting that gives the absolute lowest predicted energy use?
No, you should not always choose the setting that gives the absolute lowest predicted energy use because you have to account for the realities of initial capital costs and human behavior. Generally speaking, upgrading the energy efficiency of a building requires some sort of initial capital cost, and as the building gets more efficient, there are diminishing marginal returns to each dollar spent on energy efficiency. Thus, as a designer/owner/builder team, part of our job is to see where the future energy savings make sense with this initial cost.
In addition, sometimes, the design goals of the building are just unrealistic with the way humans will use the building. A lot of this can be fixed with automation (like with occupancy sensors), but there is still some major uncertainty with how humans will use the building.
How can you use Insight feedback to make design choices regarding materials, lighting, PV, etc.?
4D simulations are often used to show the construction sequence for an entire project, but shorter simulations that focus on a specific period of time are also useful.
Often, as a project approaches a stage of construction, that specific stage will be more thoroughly flushed out such that the designer, owner, and builder all know what's going on. This helps managers give clearer instructions to subcontractors and keep the project on schedule. Because there's all an immediacy to the plans at this stage, there is a much lower chance that such plans will not be realized and thus, more detail can be added to the simulation. Such a simulation could also demonstrate the errors in the initial schedule and any conflicts that were not first foreseen.
A detailed simulation could also be helpful for a particulate phase of construction that requires specialized equipment or has very detailed or confusing designs. I remember working for SFMTA on a construction site, and there was one particular building element that was giving everyone an enormous headache. The element was simultaneously ambiguous, unsafe, and unbuildable; thus, this portion was modified nearly thirty times before the final design was built on site. While a Revit model would have likely solved any design conflicts, a 4D model would have shown if it was buildable.
What level of detail should be included in a 4D simulation?
- Should you include all of the elements in the building model?
How can the feedback shown in a 4D simulation help you to optimize the project schedule?
- What are the main benefits of linking model elements to the project schedule?
How can model-based quantity takeoff improve the design process?
How can designers improve their designs using the information provided by preliminary estimates of the cost of building their design ideas?