Cove.tool’s holistic cost vs energy optimization feature is one of the software's most valuable and intelligent features. The optimization is designed to help users parametrically explore building component combinations to make cost-conscious and performance-driven decisions as quickly and often as possible. By running hundreds of full building simulations instantaneously while factoring in costs and savings, cove.tool can ensure every design combination is explored, and lowest cost strategy can be found regardless of the design parameters. With cove.tool's optimization feature, users can keep adding new options, changing their design objectives, and exploring and expanding the analysis parameters and will always be able to get a good idea of their projects impact on energy use, cost, and much more.
What is Optimization?
Optimization is a central concept of Machine Learning, where-in a machine is given a set of available alternatives and searches through all possible combination for solution(s) that best meets the program's objective(s). The patent pending optimization algorithm in cove.tool has the objective of optimizing for performance and construction cost. Alongside this optimization, the user is provided sliders to auto optimize and select the option that is most suitable for their design. This can be based on LEED Points, Energy Savings, Payback Years, Team feedback and more.
How Does it work?
Many times, users are exploring the impact of improving wall insulation, followed by the impact of improving roof insulation, followed by HVAC and so on. Since in a holistic performance exploration, every variable has an impact on each other, cove.tool allows the users to test the model with a more well rounded approach. In these scenarios, a user can find, that perhaps from a overall cost vs energy standpoint, it is better to use a better wall insulation and glass product, while still choosing a slightly lower performance HVAC as an example. The Optimization feature in cove.tool is about switching out thousands of combinations and comparing them to the baseline energy page. By using cost as a decision making factor, users can then identify a more cost effective way to reach a lower energy use design and/or find the optimal bundles which meet code. By optimizing, users are able to find how low they can go, in terms of energy, LEED points, Payback years, and cost. The more options you give the more likely the optimization engine will be able to find an even lower EUI and project price tag.
The optimized bundle is the best possible improvement from baseline, which lists the premium it will take in order to reach the lowest most ideal EUI target. The bundles which follow the optimization are order to ranks the 2nd, 3rd, and so on, best bundle package, If you want to add parameters to the bundles you would like to see, then you could do so by holding down on the mouse to highlighting a portion of the vertical axis which depicts the range of options you would like to analyze. This will filter out the new best bundle based on the inputted parameters.