I’ll start with the short answer to that question, YES.
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.
What is COVE?
Once the design team has moved forward from the initial massing stage, after considering various alternatives and selecting a smaller list of options, they proceed to the next stage of the design where they make material and technology choices. There are large numbers of building material, system and technoloy types, come in to the market each year with varying costs and performance properties. 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 biggest bang for the buck. Below is a case study showcasing how the use of parametric workflows and cost-vs.-energy optimization yielded higher value to the client in terms of a better and more efficient building for a lower cost.
This method also utilizes the fast energy engine 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. Running the material and technology optimization process on this building yielded an additional cost of $883,065 for a 60-percent energy performance improvement compared to that 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.
The material and technology optimization allowed the design team to analyze 186 more material and technology combinations than the typical three or four, for a 60-percent energy savings and eight years payback time at minor additional process cost. This can yield highly accurate and useful results with an accurate and diverse cost estimate. Since contractors often offer only one material and technology pricing option, the material and cost optimization is approximated using industry standard cost estimating tools such as RS Means. Future suggestions include proposing the creation of an integrated contractor and mechanical engineering team to allow for more accurate inputs to increase the accuracy of the optimization process and reduce any duplication of work.
Owners and developers must be savy about what kind of architects they hire in the coming years. Many firms talk a good game but are struggling with workflows that are unresponsive to the big data environment in the AEC industry. The owner’s architect should be able to provide designs that feature not only fancy graphics but hard data for appropriate decision making.
Click here for a demo.
www.patternarch.com – Performance Driven Design Decisions
by Sandeep Ahuja and Patrick Chopson
About : Pattern r+d, founded in 2014, enables the design of sustainable and healthy building environments and communities by developing and applying innovative tools, processes and education. Under the direction of Sandeep Ahuja and Patrick Chopson, Pattern continually reimagines the role of the energy and daylighting consultant. Pattern delivers highly integrated design solutions that go beyond shallow notions of sustainability to true parametric and performance driven design.