A building may have several suitable façade, shading, insulation or mechanical system options. Understanding their combined effects on energy, comfort and cost is not always straightforward. Building optimisation supports comparison against defined objectives and makes the reasoning behind a decision visible. It starts with a clear design question.
Why begin with a question?
“The best building” is not a complete study scope. Teams may prioritize lower energy demand, more balanced indoor conditions or a solution that fits the budget. Establishing which objectives matter together helps identify meaningful design variables. The results can then remain connected to the project's actual needs.
A study of glazing distribution, for example, may consider energy and daylight together. Another project may need to focus on the mechanical systems approach. Rather than apply an identical list of alternatives to both, the analysis should address the decisions that remain open. Those questions establish the scope.
Parametric analysis and optimisation
Parametric analysis compares results as selected design variables take different values. Optimisation uses methods that search for options meeting defined objectives well. Both can support the use of energy models in design decisions. The national laboratory's OpenStudio Analysis Framework also addresses parametric study and optimisation as different analysis purposes. NLR — OpenStudio Analysis Framework.
Owners need to understand which options are being compared and why, rather than rely on a method's name. Presentations should explain the starting conditions, changed decisions and indicators. Producing many outputs alone does not establish a sound decision. Consistent comparison and practical alternatives determine the value of the findings.
Review the starting information together
Analysis uses geometry, envelope, systems, use and climate information. It therefore matters which design the initial information represents. Unconfirmed information should be identified as an assumption. As the design develops, inputs can be updated and their consequences reviewed. DOE — Building energy modelling.
When several inputs change simultaneously, explaining the cause of a different result can become harder. The team should establish which decision it wants to investigate. Results may then be considered in appropriate groups of design options. The aim is to understand why an option performs as it does, rather than simply see an attractive result.
Balance energy, comfort and cost
A lower calculated energy demand does not establish that an option meets every project need best. Initial investment, usability, maintenance and comfort can also influence the decision. Connecting them to the objectives helps show trade-offs between priorities rather than focus on a single outcome.
A more complex solution, for example, may offer a technical advantage while requiring consideration of how the operator will use it. A low initial-cost alternative may also have different operational implications. A useful presentation explains these relationships and the reasoning that makes an option preferable.
Use the results in design meetings
A comparison graph or summary should explain its indicators clearly. Energy consumption, energy cost and emissions should not be presented as the same result. The team should understand the common basis of comparison and which conditions were maintained. Owners, architects and engineers can then discuss the same evidence.
After selection, the analysed decision should reach the construction documents. Later product or system proposals can be reviewed for their effect on that decision. The study's contribution includes this connection to the design, alongside preparation of the results file.
AECO's building optimisation service defines objectives, design comparisons and reporting according to the project stage. Sharing the alternatives under consideration helps establish the study. Our energy modelling article explains the underlying approach.