In accordance with its stated objective of reducing greenhouse gases, the Federal Government has mandated ongoing improvement in the energy efficiency of new buildings through related and continual updates to the Building Codes, which are formulated under its direction, and which govern the requirements of new building construction. With every update to the Energy Code component of the Building Codes comes more stringent energy performance requirements. In the absence of specialized insight, adherence to these increasingly stringent energy performance requirements is certain to add substantial capital cost to the construction of new buildings.
Building energy simulation (aka energy modelling) is an established means of verifying building design compliance with energy codes, as well as other building energy performance targets (ex. net-zero, LEED, etc.). Energy modelling is also established as the key tool for building design optimization, though it is not as widely used for this purpose, and the range of project-specific design combinations explored has generally been significantly limited. We can say that the range has been limited because, of the at least 10 recurring building variables which influence energy, consideration of only 3 options for each variable would equate to almost 60,000 unique design combinations. The prevalent scope of building optimization, if/when used, only typically evaluates on the order of 40 of the almost 60,000 combinations. The range of options explored has been historically so limited because the process of constructing and running the simulations for each unique option is typically a complex and time-consuming task, usually performed by a building energy simulation expert. Numerous simulations are thus costly, inclining design teams to confine their investigation to limited variation of a few parameters deemed most significant, as based on past experience. Furthermore, there is rarely any cost-benefit analysis performed on a limited range of findings.
Through the use of energy modelling and the proprietary SaaS application it has developed, ECO Matrix Inc provides the specialized insight that allows for identifying the unique design solutions, from amongst practically all of the possible design options available to a specific project (over 1 million), that will minimize the cost impact of achieving its energy performance target. Rather than arbitrarily selecting from a small number of design combinations assumed to yield the greatest benefit for the project, upon generation of a 3-D, project-specific building model, using the ECO Matrix SaaS, an energy simulation result is calculated for each unique combination derived from ample variation of each of the following variables: walls, roofs, floors, windows, shading devices, window-to-wall ratio, lighting, daylight sensors, HVAC systems, heat-recovery device, etc as indicated in below image.
The full range of energy results is then compared with a performance benchmark that is calculated using the same building model but simulated with default parameters prescribed by the energy code. Any unique solution that outperforms the benchmark is identified as a code-compliant option. Red and blue splines in the image below represent 2 unique design combinations, with each intersection indicating a variable selection (from left to right) and terminate by identifying their respective % energy savings relative to the (minimum) energy code performance benchmark.
While confirming building code compliance is important, the most powerful feature of ECO Matrix Inc’s SaaS is its ability to identify the estimated capital and operating costs of each unique solution, relative to the benchmark. This is to say that it allows for finding practically all possible design solutions and then allows for ranking the valid (code compliant) results by their relative cost difference relative to the benchmark.
One might intuit correctly that sifting through over 1 million results could be a daunting task; however, ECO Matrix SaaS elegantly addresses this challenge with an all-inclusive interactive dashboard that permits designers to visualize and filter all of the possible design options and corresponding energy simulation results on a single screen. From this graphic user interface, the design team can readily identify their project’s optimal energy efficiency improvement to capital cost premium ratio. Below is a snapshot of the project-specific ECO Matrix dashboard which displays all of the possible project-specific design options, and their corresponding simulated energy results and associated capital cost premium.
The incredible prospect of being able to find ‘best’ or ‘near-best’ design solutions on any given project is attributable to ECO Matrix’s innovative and proprietary approach to automation of the execution of hundreds of building energy simulations for a wide range of variables, each with an editable associated cost.
At the core of this innovation is the use of predictive machine learning technology for big data polynomial extrapolation; as a result, ECO Matrix can simulate and extrapolate the results for up to 1 Million different design options. The ECO Matrix technology has been developed with over 4 years of research and constant attention to rapidly changing industry needs and trends. We believe it to be a go-to solution for every designer as they start realizing the real benefits of Building Energy and Cost Optimization, provided by ECO Matrix.
Since the services provided by ECO Matrix are based on SaaS (Software as a Service) business model, consultancy on each project will follow the methodology as described below.
Over the past two decades, the design and construction industry has transitioned away from pure pragmatism towards innovative methods that help realize results that are both energy efficient and environmentally responsive. Many premium builders and developers are making efforts to achieve net-zero building energy targets and secure a category of first movers and pioneers in this field. While there are many advantages of being a pioneer, bearing the costs of ‘inventing the new wheel’ is not one of them; there is a probable risk of overspending to achieve targets in the early days.
Due to limited time and budget on every design and construction project, there is only a certain amount of time that can be spent on energy efficiency analysis for every building. As explained earlier, with the conventional design practices, design decisions are most often made after consideration of only a limited number of variables (at best) and supporting holistic cost-benefit analysis is rarely conducted. In the context of energy codes becoming ever more stringent, it is evident that the building industry is in real need of a methodology to deliver cost-optimal projects.
