Can you guarantee that the completed building will match the performance predicted by the analysis in its day-to-day operations?
- Why or why not?
No one can guarantee that the completed building will match the performance predicted by the analysis in its day-to-day operations. Since models do not contain all possible scenarios or circumstances, they cannot perfectly predict the performance of a building. However, that does not mean that models are useless. On the contrary, models provide a valuable tool to estimate the relative importance of different design decisions. For example, suppose the model predicts a 14% lower EUI (than average) for a building with more efficient solar panels. When the building is actually built and the solar panels are installed, the building only has an 8% lower EUI than average. Technically, the model is incorrect and failed to predict the exact performance. Regardless, the model provided useful insights on ways to make the building more efficient. In the words of British statistician George Box, “All models are wrong, some are useful.”
When choosing settings for each of the building performance factors, should you always choose the setting that gives the absolute lowest predicted energy use?
When choosing settings for each of the building performance factors, you should not always choose the setting that gives the absolute lowest predicted energy use because those options may be infeasible. Instead, users should seriously evaluate their situation and input reasonable values in the simulation. For example, if a company is designing a new office building on a tight budget, it is unlikely that they will cover 90% of the roof with high efficiency solar panels. On the other hand, an ecologically minded startup may be more likely to do so.
How can you use Insight feedback to make design choices regarding materials, lighting, PV, etc.?
Insight feedback can be directly used to make design choices regarding materials, lighting, solar panels, and more. For materials, Insight allows you to see the impact of different types of insulation. If it is clear that one type of insulation will drastically improve your efficiency, it is likely worth the additional cost. On the other hand, if insulation makes little difference in the overall building efficiency, it is likely not worth it. With respect to lighting, Insight allows you to toggle lighting efficiency and active light control systems. By giving designers a target lighting efficiency, they are more likely to make ecologically friendly lighting decisions. Similarly, Insight enables designers to see the tradeoffs of solar panel efficiency, payback limit, and surface coverage. While the general trend is obvious (a greater number of more efficient solar panels leads to a more energy efficient building), it can be insightful to see the incremental difference between different cost tiers.
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.
- Can you provide examples of how a simulation that focuses on a 1 or 2 week period could be useful for planning?
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?