Approach of establishing a high-resolution shading occupant behavior model in the office building

Abstract

Occupant behavior in buildings is becoming a critical factor in determining indoor comfort and energy consumption performance. The diversity and uncertainty of occupant behavior affect the implementation of occupant-centric building operations. However, existing shading behavior models do not account for various shading states (fully open, fully closed, or partially open, significantly deviating from real-world scenarios), accessibility of shading behavior (ASB), room configurations (single-person rooms and multi-person rooms), occupancy, and other factors, resulting in lower model accuracy. This study aims to address these limitations by proposing a high-resolution shading occupant behavior (HRSOB) model that integrates occupancy considerations, accessibility of shading behavior, and data mining techniques. This HRSOB model can classify various shading occupant behaviors and be integrated into the optimization controls of occupant-centric buildings. The utilization of Markov chain occupancy models significantly improves the accuracy of predicting stochastic shading behavior. The ASB classification method analyzes the features of the occupant shading adjustment behavior (hereafter referred to as shading behavior) and objectively reflects shading behavior patterns using spatial features, thereby enhancing the resolution of the shading occupant behavior model. Data-driven techniques, such as feature selection and soft voting, enable the analysis, training, and application of shading behavior data, and rigorous evaluation and validation of the HRSOB model are conducted. The results demonstrated a 10.92% improvement in the predictive performance of the HRSOB model compared with the typical shading occupant behavior (SOB) model.