Adaptive Energy-Harvesting Façades: A Multi-Technology Approach for Optimizing Indoor Comfort and Sustainability

This study presents the development of an advanced façade system that integrates energy harvesting and Machine Learning (ML) to enhance thermal regulation and user comfort in residential buildings. The proposed system consists of automated outdoor blinds equipped with photovoltaic panels that dynamically adjust their orientation to maximize solar energy capture throughout the day. To further optimize indoor environmental conditions, low-cost screen-printed temperature, humidity, and carbon dioxide (CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>) sensors are embedded in the window frame, enabling real-time monitoring of ambient parameters. These sensor inputs support predictive control of window operation and climate regulation. Additionally, a mobile application allows users to monitor environmental conditions in real-time, manually control window settings, and receive energy efficiency recommendations. This research was conducted as part of the iPOWER project within the ILLIANCE consortium, which aims to develop innovative energy-saving solutions for sustainable building management