The first look at the baseline results page can sometimes have an unexpectedly high, low, or unfamiliar outcome. This article discusses the fundamental concepts of combining and calibrating existing building standard, simulation guidelines, and open-source databases to output your building's baseline and early-stage design performance metrics.
To understand how cove.tool generates your projects initial building performance metrics, users will first need to understand what ASHRAE, CBECS/RECS, AIA 2030, and LEED are. These four organizations and their resources combined may explain why your building has the performance outcome it has and why.
ASHRAE - a national building standard published by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) which serves as a governing body of technical standards and guidelines for indoor-environment-control technology, such as heating and air conditioning (thermal comfort), ventilation, and emissions (air quality), and building energy conservation. Every US State references a version of the ASHRAE Building Code, or an equivalent standard, for its energy code and design guidelines. Serving a similar purpose, Title 24 is the Building Energy Efficiency Standard from the state of California. It is designed for new and existing construction to achieve energy-efficient designs and preserve environmental quality. For the purposes of this article, Title 24 will fall under the ASHRAE equivalent energy code umbrella term.
CBECS - the Commercial Buildings Energy Consumption Survey, which is the governing surveyor for the collection of national energy use data for commercial buildings types. This information is used to differentiate commercial buildings types into categories whose program/uses share similar energy usage data (ex. schedules, energy consumption, and expenditures, and more).
RECS - similar to CBECS but for the residential market, is the Residential Energy Consumption Survey. This organization has the same job as CBECS and is crucial to creating a Residential Template.
AIA 2030 - AIA 2030, is a national effort to encourage an industry-wide commitment to prioritize building energy performance and reduce building-related impacts on the environment. So far it has created a framework for standardized reporting, guidelines for calculated energy baselines and energy use percent reduction targets, as well as publish several online tools to calculate your various performance-related metrics. Every year leading up to 2030, AIA will upgrade the guidelines and minimum performance targets, until every participating firm should smoothly achieves Net-Zero ratings for all their projects.
LEED - Leadership in Energy and Environmental Design, LEED is the most widely used green building certification and rating system in the world. A division of the U.S. Green Building Council (USGBC), LEED provides a framework that project teams can apply to create healthy, highly efficient, and cost-saving green buildings. The framework includes guidelines, calculation methods, and best practice assumptions for the optimization of building location and planning, sustainable site development, water savings, energy efficiency, materials selection, waste reduction, indoor environmental quality, innovative strategies, and attention to priority regional issues.
Before we begin and as a reminder, cove.tool is an early-stage building performance analysis and guidance tool. The tool works best as the first step in a multi-step process. As early-stage analysis becomes integrated into everyone's workflow, these rapid style analysis tools are 1) the key to identifying where and when to follow up with more detailed analysis, and 2) eliminating the stigma that has deterred and/or gate-kept the running of compliance grade simulation, especially when we have the science and experience to back up the legitimacy of these types of tools.
UNDERSTANDING THE BENCHMARK
Understanding the wheels that turn in order to generate the Benchmarking diagram (above) is key to understanding your building's energy performance results.
WHERE DOES THE BASELINE COME FROM?
The 2030 Baseline is based on the 2003 CBECS and RECS 2001 databases. This value describes the energy consumption anticipated for a typical modern building, of your project type and location.
WHERE DOES MY EUI COME FROM?
Your EUI or Energy Use Intensity, also known as Whole Building EUI or proposed design EUI, comes from simulating your building design with the ISO 13790 Heat Balance Engine, using the prescriptive inputs from your selected energy code, and most local and recent climate data (weather file).
WHERE DOES THE ENERGY TARGET COME FROM?
Your 2030 Target is based on a percent reduction target set by the AIA 2030 Challenge. This percent reduction target changes every couple of years and is currently set at a 70% reduction from baseline (2015-2019). Next year it will drop to an 80% reduction.
So now that we have explained where the metrics come from, let's try to identify why your results can seem higher or lower than expected.
Why are the results of my project 60% lower than the baseline without any changes or optimization?
This article uses the FAQ above as an umbrella phrase. The percent difference from your expectation and reality, vastly varies for every project yet the reasons for over and/or underestimations may be as follows:
A lower EUI than expected can be caused by a discrepancy between CBECS and your selected energy code.
The truth is the 2003 CBECS/RECS 2001 database uses a prototype model of your project which hasn't changed from the moment of its initiation. These frameworks are still the national standard for energy and building performance simulations. By pairing the baseline framework with a further advanced energy code, the modernization of inputs can vastly highlight the disparity between the baseline model and the proposed design model. cove.tool calibrates the CBECS 2003/RECS 2001 data by adding climate, weather, space type, building size, occupancy, and schedule details, but the most recent energy codes are typically still significantly more efficient than the baseline models. For example, the 2013 ASHRAE 90.1 is typically a 50% reduction from baseline on average. See the table below which tracks the improvement of energy codes standard for energy use efficiency.
Because the 2013 ASHRAE 90.1 was written with a more recent understanding of technological capabilities and control strategies, the values are far more efficient than the 2003 CBECS/RECS 2001 models which are used to calculate baseline. This, among many other reasons, is why we have the AIA 2030 Challenge which forces design teams to compare their performance results, not to the baseline, but to the 70-80-90% reduction target. Even in the calculation of your LEED Points - v4.0 - 4.1, EAc2 (Percent Improvement in energy performance), the baseline calculation is solely calculated with the ASHRAE 2010 energy code, regardless of your elected version. If you are in one of the 27 US States which have no required energy code, or a version predating the ASHRAE 2010 (or equivalent), then your reduction from baseline would not be a useful indicator of the amount of LEED Points you could receive.
Changing values is the manual way to reduce energy consumption for the building. However, these changes will not change your affect your baseline value. Upgrading energy code versions will also get you different engineering values, and maybe a good way to start seeing your building in new, more efficiency-conscious light. Some inputs are the same from one code version to the next, but as a package together they take one step forward in sustainable design.
Other Causes for Unexpected EUI/Baseline Differences
A lower EUI can also be explained by an incredibly efficient design. Yes, some building designs can be incredibly efficient without any changes to the automated baseline values. This can occur when the energy code inputs are stringent for the building type, if the building location requires little-to-no significant heating and cooling, or if your building is considerably small. Also, a model with a small glazing area can be surprisingly efficient.
An inexplicably high Baseline EUI can be caused when attempting to model a custom building template. When creating a custom building template, users are required to complete some additional steps to accurately align a cove.tool listed template, to a framework, that more accurately relates to the user's project. Currently, the workflow is set to allow users to select from a list of the existing cove.tool templates for their Baseline Calculation. In this case, before any changes inside the project, Your EUI and breakdown will be correct, but the 2030 Baseline and 2030 Target may not exactly translate for the project type you have. At this point, users can reach out to the cove.tool support team, who will accurately calculate the Baseline and Target EUI for the project. Or users can calculate try finding these metrics themselves, by using the AIA 2030 Zero Tool calculator.
Other Baseline Metrics FAQs
Where do the carbon-based metrics come from?
Cove.tool uses Zero Tool to calculate the Carbon-based metrics. Read more about this process here.
Where does the EUI Breakdown come from?
The whole building breakdown is the result of the Heat Balance Engine simulating your building's energy use during a typical year in the climate conditions typical to your building location. Every input in the project, including climate, weather, space type, building size, occupancy, and schedule will influence the extent of energy use each breakdown category require to support the building.
Where do the Utility Costs come from?
The utility rates come from a national database of utility rates per state, published by the Department of Energy. For locations outside of the United States, users can edit the value under the general tab. Users will need to know the cost per kBtu for Electricity and Gas.
What does Optimization do, that the baseline results can't?
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, we 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 should be able to find how low they can go. The more options you give the more likely the optimization engine will be able to find an even lower EUI and project price tag.