September 5th 2019

Martin Gough Student Award 2019 - Winner's Case Study

Martin Gough Student Award 2019 - Winner's Case Study

2019 marked the second year of the Martin Gough Student Award. This year, the top prize went to Bayan Khayat-Sarama of Newcastle University for her paper on ‘Designing a near-Autonomous House in Palestine.’

Read on to find out more about Bayan’s winning paper…

Bayan’s research focused on the application of passive design strategies and renewable energy solutions to design a near autonomous house in Palestine. The study also looked at ways in which to achieve optimum air quality, visual and thermal comfort from the early design phase.

Historically, Palestine has faced energy shortages as a result of the Israeli occupation placing limitations on the accessibility of resources. Furthermore, global climate change has contributed to an increase in average annual temperatures, which has led to uncomfortable conditions in many homes. Addressing the energy deficit and thermal comfort issues were therefore key factors driving Bayan’s analysis.

Bayan created a base case model in the IESVE of a detached, two-story housing unit within a proposed small-scale remote neighbourhood development in Area C of Palestine.

The building’s orientation, thermal insulation, window-wall ratio (WWR) and solar shading were assessed to identify possible design interventions to provide appropriate internal daylighting, natural ventilation and operative temperature.

Using IESVE software, Bayan created the base case simulation model according to typical construction practices and materials used in Palestine. This base case was then compared with an optimised IESVE simulation model which used upgraded, available and affordable materials to improve the performance of the building envelope. Illuminance and daylight were enhanced within the optimised model by making changes to the WWR and building orientation, while Solar PV panels were also added to the optimised design in order to reduce energy cost. Several applications within the IESVE software were used including ModelIT, MacroFlo, MicroFlo and ApacheSim.

Through her analysis, Bayan was able to demonstrate that optimising the insulation, WWR and building orientation of the model would improve the ventilation rate and illuminance significantly. Indoor air quality was upgraded by up to 84% while CO2 concentration levels were reduced by 10% during warm months. Visual comfort and illuminance were improved by 32%

It was determined that the optimised design could achieve the same level of thermal comfort as the base model while using 4747.8KWh less energy, the equivalent of 744 additional hours of thermal comfort. Meanwhile, the addition of solar PV panels provided a further 24% annual energy savings.