Window state or action modeling? An explainable AI approach in offices

Window operation significantly impacts energy use and indoor environmental quality in buildings. Individuals behave differently, making it difficult for models trained on a specific dataset to encompass the entire spectrum of these actions. A generalizable model is essential to predict the behavior of diverse occupants in office spaces. To address this need, this paper presents a systematic approach that captures this diversity, thereby contributing to developing a model towards generalizability. The approach involves state and action modeling through a Random Forest algorithm on the ASHRAE Global Occupant Behavior Database. The data pre-processing, hyperparameter tuning, and evaluation are deeply described and applied to window action and state datasets. Our results demonstrated that including metadata in a state model and applying a G-Mean threshold moving technique can result in an F1-score of 0.74. This score slightly outperformed the state room-wise model, which was trained only on its own dataset and achieved an F1-score of 0.73. However, both models had similar accuracies of 77%. The action model did not fare as well as the state models, with an F1-score and accuracy score of just 0.42 and 49%, respectively. In contrast, the action model showed more explainable results for domain experts than state models.