cove.tool Seeks to Stop Climate Change One Building at a Time.
With energy codes updating across America, many developers are rightly worried about the rising costs associated with compliance. The ultimate goal of these codes is to reach net-zero-energy buildings by 2030. Simply carrying on in business as usual fashion is a recipe for dramatic increases in cost per square foot. The AEC industry in general relies heavily on the belief that what worked on the last project will work for this one. Most architects and engineers comply with the new energy codes by specifying the most expensive systems, wall types, windows and control options. However, new processes and advanced algorithms are giving owners the ability to optimize for first costs to make better decisions on energy.
That new way of working is to adopt a big data approach with all the options on the table, using rigorous metrics to ensure that the right decisions are being made.
Design objectives such as program, construction cost, environmental performance and aesthetics are key factors in an architectural design. Conceptual design decisions about a building’s orientation, massing, materials, components and systems largely determine life-cycle costs of a building. However, with multiple objectives and constraints, the number of decisions quickly spirals out of control. In many cases, limited time and budget restricts the set of design options that can be tested during conceptual design. This leads to design solutions with poor initial and life-cycle performance. Thus, owners need the power of computation and big data to find low cost solutions that still comply with the code. It will not be possible to build any building without simulation within the next five years. As the level of complexity increases, the number of variables considered for analysis should also increase.