Occupant-centred solar-responsive facade assessment for educational spaces

This project developed an occupant-centred method for evaluating solar-responsive facade options in a shared educational space. It combines feedback from building users, short-term environmental measurements and whole-year simulation so that glazing and shading decisions are based on both physical performance and occupants' experience.

The case study was a 297.9 m² south-facing study area in Building 25 at Politecnico di Milano. The research was co-led by Elnaz Safari Abyazani and Shima Zibakalam under the supervision of Prof. Andrea Giovanni Mainini. The occupant-study component later contributed to a peer-reviewed conference publication with additional collaborators.^[1]

Facade design is usually assessed through standard daylight and energy indicators, while occupants may experience glare, excessive solar exposure, visual discomfort or uneven conditions within the same room. The objective of this work was to connect these two perspectives and identify facade interventions that could reduce unwanted solar exposure without sacrificing useful daylight.

The study asked:

• How do measured environmental conditions relate to students' comfort and seating preferences?
• Where do daylight sufficiency, glare and excessive solar exposure occur in the existing space?
• How do alternative glazing, shading and micro-structured solar-control solutions change annual performance?
• Can the results be translated into zone-specific recommendations rather than a single uniform facade solution?

Three questionnaires were completed by 384 students. They addressed thermal and visual comfort, daylight, glare, outside view, visual privacy and seat choice. The field campaign recorded:

• Illuminance;
• Air temperature;
• Relative humidity;
• Mean radiant temperature; and
• Air velocity.

The measurements were used to interpret the questionnaire responses and to document the environmental conditions during the surveys. The published occupant study found that thermal and daylight conditions strongly influenced perceived productivity and comfort. Participants sometimes accepted illuminance above the standard design target, and visual privacy was more influential in seat choice than the outside view.^[1]

A whole-year ClimateStudio model was developed using Milan-Linate weather data. The 297.9 m² analysis zone was represented by a 772-point workplane at desk height. The baseline assessment included spatial daylight autonomy (sDA), annual sunlight exposure (ASE), spatial daylight glare (sDG), illuminance, daylight factor and a conceptual annual energy balance.

The design study compared:

• 10 glazing assemblies;
• 9 shading configurations; and
• 5 MicroShade scenarios represented with custom BSDF files.

Each option was assessed across several indicators rather than ranked by a single value. This was important because reducing solar exposure or glare can also reduce useful daylight, while occupant preferences vary across the room.

Across the tested MicroShade assemblies, ASE was reduced from 16.2% in the baseline to 0–1.1%. Spatial daylight glare was reduced from 29.2% to a range of 10.7–24.1%, depending on the assembly. The findings were therefore translated into zone-specific recommendations that balance solar exposure, useful daylight, glare, conceptual energy performance and occupant preferences.

The following sequence can be adapted to other educational or public buildings:

1. Define the decision problem, study zone, occupied hours and facade alternatives.
2. Prepare an occupant questionnaire covering both environmental satisfaction and spatial behaviour, including seat choice.
3. Record environmental conditions at the time and location of each survey. Do not interpret subjective responses without their physical context.
4. Create a baseline annual model and document weather data, geometry, materials, schedules, sensor grid and metric thresholds.
5. Map sDA, ASE, glare and illuminance spatially. Avoid relying only on room-average values.
6. Test glazing and shading alternatives with consistent inputs; use BSDF data when modelling complex optical systems.
7. Compare each alternative against several indicators and identify trade-offs.
8. Combine the simulation maps with occupant evidence to make zone-specific recommendations.
9. Report assumptions, uncertainty and data-access restrictions so that another team can audit or reproduce the method.

For a reproducible workflow, each observation should use an anonymous participant or session identifier and include timestamp, seat or zone, questionnaire responses and simultaneous measurements. Each simulation scenario should have a unique identifier linked to glazing properties, shading geometry or BSDF file, schedules, weather file, metric settings and results. Personal identifiers should not be collected unless they are essential and ethically approved.

The original thesis used specialised simulation and BIM tools and was not released as an open-source project. The raw questionnaire data, digital model and custom material files are not publicly available; any release would require anonymisation, permission from the research team and confirmation of institutional and licensing requirements.

This Appropedia page openly documents the reusable logic of the study. A future permission-cleared package could add:

• a neutral data dictionary for occupant and measurement records;
• scripts for cleaning exported survey and simulation results;
• a synthetic example dataset;
• explicit metric and modelling assumptions;
• guidance for low-cost field validation; and
• links between facade scenarios, IFC information and life-cycle or energy indicators.

The master's research was co-led by Elnaz Safari Abyazani and Shima Zibakalam under faculty supervision. Elnaz contributed to research design, questionnaire and fieldwork activities, digital modelling, annual simulations, comparative analysis, visualisation and preparation of the resulting manuscript. The conference paper was co-authored by M. El Shemy, D. Jiménez Herrera, E. Safari Abyazani, S. Zibakalam, E. Casolari and A. G. Mainini.^[1]

• Peer-reviewed publication
• Elnaz Safari Abyazani's Appropedia profile