Sensor-free machine learning framework for energy-optimised blind angle control in office buildings

Adaptive facade systems play a crucial role in improving energy efficiency and visual comfort in office buildings, yet their operation often depends on sensor-based feedback or rule-driven logic that limits scalability and robustness. This study introduces a sensor-free machine learning framework for optimising venetian blind slat angles using only weather and temporal parameters. A south-facing single-zone office in Munich, Germany was modelled using Honeybee/EnergyPlus to generate a simulation-based dataset of hourly energy performance under varying slat-angle configurations. Two supervised learning models, Random Forest and XGBoost, were trained to solve a multi-class classification problem in which each hourly sample was assigned one of 18 discrete blind-slat angle classes (0°-170° in 10° increments), representing the energy-optimal configuration. Among the tested models, XGBoost achieved the highest predictive performance with 88% accuracy and a circular mean absolute error of 3.61°. When implemented for adaptive control, the proposed strategy reduced total summer energy consumption by 21.4% compared to the best-performing fixed-angle configuration. The results demonstrate that simulation-based, data-driven shading control can effectively replace sensor-dependent systems, offering a scalable, low-cost solution for intelligent facade management and energy optimisation in modern office buildings.