Big Data for Building Decisions – cove.tool
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.
The manufacturers of building materials, systems and technologies continue to create larger palettes of products with varying performance and cost. This variety allows for a vast array of alternatives available for buildings, resulting in very large numbers of technology combinations.
For example, given 16 technology types, each with three possible options for performance and cost choice, would yield to 4.3 million unique combinations. When a contractor and architect make choices, they are unable to perceive all of the choices and their impacts collectively. This leads to making inefficient choices in terms of either energy or cost or both. This is where, in our Big Data world, cost-vs.-energy optimization can prove useful to get the highest bang for the buck.
The COVE method utilizes a fast normative energy engine based on the European ISO 13790 for finding the optimal mix of technologies. To illustrate the use of this method, we collaborated with a senior project designer and his team to analyze a 156,000-sq.-ft. (14,492-sq.-m.), eight-story office building in Charleston, S.C., as a case study. As communicated by the design team, this project near the river had employed the rule of thumb approach to come up with a design. All selections were highly typical of office building construction in the Southeast to maximize the functionality of the design. The building is a typical cast-in-place concrete structure with a continuous glass facade on all sides. As designed, the office building performed just slightly better than the ASHRAE 90.1 2013 baseline.
Since the overall estimate for the building was $27 million, a 60-percent (4.03 Kwh/sq. ft ) reduction in energy was achieved for a 3.3-percent increase in the estimated cost. With the current price of electricity approximated at 9 cents/kwh, this meant a payback time of 10.4 years. The estimated payback time is likely to decrease with the rise in electricity costs in the upcoming years, demonstrating the achievable impact of this process from an energy and business perspective.
Emerging tools are enabling integrated modules and scripting environments that are narrowing the gaps among architects, engineers and computer programmers. The rapid feedback method can use optimization to create a design space to select materials and technology options while balancing cost vs. percent of energy savings. Building upon this first step, the mass optimization process enabled the generation and analysis of orders of magnitude more design alternatives, thus allowing the exploration of designs that were substantially more energy efficient than those typically evaluated using current methods, at negligible additional process cost. This can yield highly accurate and useful results with an accurate and diverse cost estimate.