In May 2018, I shared a simple Excel workbook that let anyone see—and play with—the relationship between the sun, façade orientation, window size, and external shading. The goal was deliberately modest: make the physics transparent, keep the formulas visible, and let stakeholders validate design choices with their own eyes rather than accept a black-box simulation.
That earlier exercise answered the first practical question: “What does the sun actually do to this façade across the year?” But the visualisation was essentially a snapshot.This follow-up blog takes the next step. Once the geometry and solar exposure are clear, the real decision problem emerges: how much shading is enough, how large should the window be, and what combination of orientation, shading depth, and glazing delivers the best trade-off between summer heat gain, winter solar benefit, daylight, glare, and first cost? These are quantitative questions. They require numbers, not just pictures.
This blog post therefore moves from visualisation to quantitative decision-making. Building directly on the 2018 model, it shows how the same transparent Excel environment can be extended into a lightweight multi-criteria decision framework. The numbers remain editable, the assumptions remain visible, and any member of the project team can still check the resulting scores. In short, the workbook that once helped us see the sun now helps us decide what to do about it.
Imagining this as a 3D model that updates with input data, as shown above, helps us appreciate the interplay of shadows from a particular perspective. This also lets you quickly validate your inputs and outputs visually. Charting, though helpful, provides only limited visualisation validation. Copilot created the 3D model from the Chart visualisation.
Now I want to put the workbook to real use by asking questions like these.
How much time does direct sunlight enter the room in a year? Or, more specifically, during a given duration, say between 10 AM and 5 PM? Or, even more ambitiously, design a shade that meets my specific glare-control, energy, and daylight-efficiency criteria while providing the requisite views.
You can evaluate any number of scenarios for optimal daylighting and passive design measures, etc. The following are daylight-related options for measuring and thus controlling glare from direct sunlight entering a living space.
The following scenarios were generated for various orientations (North, North East, East, South, and South West) and projection widths of 1, 0.5 meters, and no shading, for a site located at 28.5 degrees latitude.
Optimal shading to avoid glare from direct sunlight. It also applies to designing solar passive habitats for thermal comfort.
Why use a spreadsheet when commercial software is available? The answer is not just transparency. Here, you are in charge and set the agenda through first principles and validations, not some black box. Construction decisions are complex; you decide what matters in context and what is optimal by your definition.
I thoroughly enjoyed the new learnings that emerged while developing this workbook. It has no passwords, so you can experiment, keep evolving it, and share feedback if you wish.
Way Ahead
While reviewing "
Sky Modelling for the Location of Gurugram, India (TERI 2022)", I observed,
"The use cases for the data compilation are limited only by our imagination." This statement applies equally well here. Perhaps daylight data collected before retrofitting old structures can be aggregated into a practical local database to support realistic daylight designs and optimise energy use. It would not require costly instrumentation but would be just as effective. I would love to join such exciting research.
As climate change drives more frequent extreme weather events, accessible tools like these can help designers, owners, and stakeholders make better-informed decisions using methods that are both transparent and adaptable.
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