•   over 4 years ago

Create a Comprehensive Dataset or Scripted Model for Rapid HVAC Comparison

Energy modelers are often asked to contrast different HVAC systems based on energy performance. Although a modeler might have done a specific HVAC system multiple times for different projects it is often not easy to estimate the energy use of a building without modeling its various details. This project proposal looks at the possibility of generating a large dataset or a fully parametrized/generalized model that can be used for all climate zones and space types to come up with benchmarking EUIs that can be used at early stages of design. The goal with this tool would be that the designers can filter through the dataset or set of inputs to find a matching set of criteria similar to their architectural design and be able to come up with EUI estimates without the need to create a detailed model. A fully parametrize shoebox model, granted it is vetted for scalability, or DOE reference building models can be used as a starting point. The final product will either be an online dataset (good for inexperienced users) or Grasshopper module (can be used by experienced users).

Why is this needed: To save time at early stage of design. HVAC comparisons and selections are usually high level conversations that are commonly overruled by cost or preference implications at design meetings. Comparing HVAC options through modeling is an intensive effort that has little ROI on the consulting services provided. In other words, detailed and rigorous modeling, including various QA and fact checking, are better to be reserved for later stages of design where aggressive goals could take advantage of the fees.
CBECS data is supposed to help with this process but the measured datasets in CBECS have holes in them, also don't usually include many of the new HVAC systems.

How can this work: The underlying assumption to aim at producing such tool is that the energy use of building is primarily driven by three main factors: 1) building architecture and ambient conditions 2) internal loads, space programming and use profiles and 3) HVAC system.
Theoretically, for a specific building type, HVAC system, and envelope properties, there should be a relatively linear relationship between the energy use and the size of the building. That is the premise that makes an index like EUI (kBtu/ft2) a useful metric while comparing different buildings. Size in this context should be a metric that captures the ratio of exterior exposure to interior volume as well as the average building internal loads accounting for diversity in the space programming. The PassiveHouse standard for example looks at the exposed exterior area of building (A) to the enclosed volume (V) as an initial compactness metric. This needs to be coupled with a similar metric that captures the ratio of different space areas relative to the main building occupancy so that the internal gains can be normalized and scaled with simple building area inputs. The user then only needs to input area information from the actual project.
If the geometry and architectural features can be simplified with only area inputs then a broad range of different thermal properties, climatic conditions and HVAC options can be evaluated with the help of some automating scripts. There are many other factors embedded in this simplification that need to be figured out. To name a few:
 Perimeter to core area ratio
 Window-to-wall ratio
 Shoebox model boundary condition and scalability question
 OA requirement rates based on areas and delivery method
 Common HVAC system efficiencies and their dependency on actual geometry

What are some challenges:
Using a shoebox approach can be beneficial to parametrize the model more easily but can also introduce the potential of missing key architectural and mechanical factors that will be present in a full-scale model. Factors such as the impact of multi-story building boundary conditions and the interactions between HVAC systems in a full-scale model versus a shoebox model come to mind. List of potential challenges:
 Plant loop implications for HVAC systems in the shoebox model versus full-scale
 Air mixing between multiple zones is limited shoebox model
 OA flow rates will need to be determined based on the main space type in the shoebox model
 Internal load diversity impact on the HVAC energy consumptions may not be easily realized
 Initial area proportions need to be realistic – DOE reference building inputs can be used as starting values
 Architectural elements such as shading devices can be more easily incorporated and parametrized in the shoebox model but may not be well representative of real-world shading potential

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