Multi-objective evolutionary optimization of photovoltaic glass for thermal, daylight, and energy consideration

Abstract

The potential of fenestration systems is increased by incorporating photovoltaic technology into windows. This recently developed technology enhances the ability to generate energy from the building facade, improve the thermal and daylight performance of buildings, and visual comfort of occupants. Integrating an evolutionary optimization algorithm into this technology is one of the possible sustainable solutions to enhance building performance and minimize environmental impact. This paper uses a genetic evolutionary optimization algorithm to explore the optimum performance of photovoltaic glass in an architecture studio regarding annual energy consumption, energy generation, and daylight performance. Design variables include a window-to-wall ratio (i. e., window size and location) and amorphous-silicon thin-film solar cell transparency to generate optimum Pareto-front solutions for the case building. Optimization objectives are minimizing annual thermal (i.e., heating and cooling) loads and maximizing Spatial Daylight Autonomy. Optimized results of low-E semi-transparent amorphous-silicon photovoltaic glass applied on the facade show that the spatial daylight autonomy is increased to 82% with reduced glare risk and higher visual comfort for the occupants. Photovoltaic glass helped reduce the selected room's seasonal and annual lighting loads by up to 26.7%. Lastly, compared to non-optimized photovoltaic glass, they provide 23.2% more annual electrical energy.