Improved building performance prediction model based on the OODA-MDOB approach

The neglect of subjective factors such as cost consideration and accessibility in occupant behavior models leads to unrealistic device usage simulations, which ultimately undermines prediction accuracy. To address this gap, this paper proposes a Multi-Device Occupant Behavior (MDOB) model that integrates the Observe–Orient–Decide–Act (OODA) loop framework. The model systematically divides the behavioral process into four stages: in the Observe and Orient stages, both objective and subjective data are collected simultaneously, and key influencing factors are identified through correlation analysis; in the Decide stage, a multi-device coordinated behavioral decision-making mechanism is constructed by combining fuzzy theory and survival analysis; in the Act stage, the behavior is executed, and dynamic feedback is implemented. Through this closed-loop modeling framework, the model achieves a more accurate characterization of multi-device coordinated adjusting behaviors. Validation results show that the proposed method achieves higher accuracy in predicting actual energy consumption and thermal comfort levels. Furthermore, simulation results based on the MDOB model indicate that, compared to standard assumption scenarios, the model not only improves thermal comfort by 4.74% but also reduces energy use intensity by 3.39 kWh/m².