Architecture Engineers Award

Building Integrated Photovoltaic (BIPV) façades play a critical role in advancing net-zero and energy-positive building strategies by simultaneously serving as envelope elements and renewable energy generators. However, optimizing BIPV façades is challenging due to competing performance objectives, particularly photovoltaic energy generation and indoor daylighting quality. This study proposes a parametric optimization framework to systematically address these trade-offs during early-stage design. Challenges in Balancing Energy Generation and Daylighting Façade design decisions, such as window-to-wall ratio (WWR), directly influence solar exposure on opaque surfaces for photovoltaic efficiency while also affecting indoor daylight availability and visual comfort. Increasing PV-active areas often reduces daylight penetration, whereas excessive glazing can compromise energy generation potential. These conflicting requirements necessitate a multi-objective optimization approach. Paramet...

Spiking Neural Networks (SNNs) represent a biologically inspired computing paradigm that transmits information through discrete spikes, offering improved biological interpretability and superior energy efficiency. Despite these advantages, the design of high-performance SNN architectures remains a major challenge, as existing structures rely heavily on manual engineering and expert intuition. This research addresses this issue by proposing a fully automated method to discover optimal SNN architectures using a predictor-assisted evolutionary neural architecture search framework, aiming to enhance performance while reducing computational cost and energy consumption. Limitations of Manual SNN Architecture Design Traditional approaches to SNN architecture development depend largely on manual design strategies, making them inflexible and difficult to scale. These human-crafted architectures often suffer from limited adaptability, restricted search space exploration, and reliance on expe...