Building Performance Simulation

A building performance simulation is a physics-based computer model that predicts how a building will behave thermally and energetically over time. You assemble a digital model of the building - its geometry and orientation, the make-up of its walls, roof and floors, the area and properties of its glazing, its shading, how airtight it is, how it is ventilated, what heating and cooling systems it has, and how it is used (people, lights, computers, schedules). You then attach a weather file - the hourly record of temperature, solar radiation, humidity and wind for the location, exactly the kind of typical-meteorological-year or future file this course has discussed. The software steps through the year, usually hour by hour or finer, solving the heat balance for each zone: sun coming through the glass, heat conducting through the fabric, warmth from people and equipment, air exchanged with outside, and whatever the mechanical systems add or remove. From that it reports outputs - indoor temperatures through the year, hours of overheating, heating and cooling loads, annual energy use, peak demand, carbon.

This is genuinely powerful, and it is why simulation sits at the centre of climate-analytics practice. It lets a designer ask 'what if' and get a physically reasoned answer while the design can still change: what if the glazing faces west instead of north, what if we add external shading, what if we increase insulation, what if a heatwave hits and the cooling is off? Crucially for this course, because the weather is just an input file, you can swap a historical file for a future one and re-run the identical building against the climate of the 2050s or 2080s - seeing how the same design copes as the world warms. Simulation ranges from quick, simplified 'shoebox' models used early in design to detailed dynamic models used by specialists for compliance and system sizing. It underpins energy codes and rating schemes worldwide, including the modelling behind India's Energy Conservation Building Code. Understanding what it is - a virtual building run against weather data - is the foundation; understanding its limits is the rest of this lesson.

Used properly, simulation is trustworthy for certain kinds of question - mostly *comparative* and *directional* ones. It is very good at telling you whether one design option is better than another under the same assumptions: does adding external shading cut summer overheating, does deeper insulation reduce peak cooling load, does a different orientation lower solar gain? Because both options are run through the identical physics and weather, the *difference* between them is far more reliable than either single absolute number. This is how simulation earns its keep in design: it ranks choices, reveals which decisions matter most, and shows the direction and rough size of an effect. A model can confidently tell you that this room will overheat far more than that one, or that the west glass is the dominant summer problem - and that guidance is sound even when the exact predicted temperature is not.

Simulation also captures things intuition misses. Heat moving through a building is dynamic - thermal mass stores and releases heat with a time lag, solar gains peak at different hours on different facades, night ventilation can flush accumulated heat - and a dynamic simulation tracks all of that hour by hour in a way no rule of thumb can. It can expose a design that looks fine on paper but overheats badly in an August afternoon, or a passive strategy that works only if the occupants actually open the windows at night. It lets you test the building against a specific severe week, or against a future weather file, and *see* the consequence. And when a model is calibrated against real measured data from a comparable building, or checked by a specialist, its absolute predictions can become genuinely useful for sizing and compliance. The honest framing is this: simulation is excellent at comparison, ranking and physical insight, good at absolute prediction when carefully calibrated and validated, and always better treated as a well-reasoned estimate than a measurement. Lean on what it is strong at - and keep the binding sizing, compliance and life-safety numbers with the qualified engineers and validated tools that produce them.

Now the essential caution. A simulation predicts how a building *would* behave *if* it were built exactly as modelled, used exactly as assumed, and operated exactly as specified - and real buildings never meet all three conditions. The result is the performance gap: buildings routinely use significantly more energy, and can be less comfortable, than their models predicted, sometimes by a wide margin. The reasons are not mysterious. Occupancy is assumed - the model guesses how many people, how long, what they set the thermostat to, whether they open windows - and real people behave differently, often using more cooling and heating than the tidy schedule supposed. Construction differs from the drawing: insulation installed imperfectly, thermal bridges, air leakage worse than modelled. Systems are commissioned and controlled imperfectly, running when they should not, fighting each other, degrading over time. Small modelling simplifications accumulate. None of this is a failure of simulation as such; it is the unavoidable distance between a clean model and a messy, inhabited, imperfectly-built reality.

The discipline this demands is humility about absolute outputs. A single predicted number - '112 kWh per square metre per year', 'peaks at 31.4 degrees' - carries an air of precision the underlying uncertainty does not support, and quoting it as if it were the answer is a real error. Better practice treats a prediction as an estimate with a spread, states the assumptions openly, tests how sensitive the result is to the shakiest of them (occupancy, air-tightness, controls), and leans on comparisons rather than single absolutes. A well-run study also says which way the gap is likely to run - almost always toward more energy and more heat than the tidy model showed - so the honest reading of a comfortable-looking result is caution, not reassurance. Where a model has been calibrated against measured data from a real, comparable building, its absolute outputs earn more trust; an early-stage model built on default assumptions earns far less, and should be quoted as a rough estimate at most. For anything binding - system sizing, code compliance, a life-safety claim about heat - the numbers must come from qualified engineers using validated, ideally calibrated tools and the governing codes, not from an architect's early-stage model taken at face value. Simulation is indispensable for understanding and improving a design. But the map is not the territory, and the model is not the building - carry that sentence into every result you read.

Everything so far applies to simulating a building against *today's* weather. Climate analytics adds a further move: swap the historical weather file for a future one - a projection for the 2050s or 2080s, often 'morphed' from a historical file to reflect a warming climate - and run the identical model against it. This is exactly what the course has been building towards, and it is the right thing to do: it lets you see whether a design that is comfortable now will overheat later, how its cooling energy climbs, whether it survives a future heatwave. Testing a long-lived building against the climate it will actually face, rather than only the one that has passed, is the whole point.

But it stacks a second, deeper uncertainty on top of the ordinary performance gap. The future weather file is a scenario, not a forecast - it depends on unpredictable emissions choices, disagreeing climate models and downscaling assumptions, so it is one plausible future among a range. Now the model's own uncertainties (occupancy, build quality, controls) are being run against an input that is itself uncertain. The danger is compounded false precision: a future simulation produces a graph as crisp as any other, and it is dangerously easy to read 'the building reaches 34.7 degrees in 2050' as a prediction of a specific future day. It is nothing of the sort. The honest way to simulate the future is as a range: run several future files - different scenarios, different decades, different models - and read the *spread* of outcomes, asking whether the design is robust across all of them rather than tuned to one. Report 'across these plausible futures, overheating rises from X to Y and the design needs shading and a passive fallback to stay safe', not a single decimal. Use future simulation to understand the direction, range and severity of the risk - and keep the binding results with the specialists, validated tools and codes. It is the most powerful thing in this course and the easiest to misuse.