Deep learning for predictive window operation modeling in open-plan offices

Abstract

This study explores how the past, both short and long-term, affects the predictive window operation modeling in open-plan offices. To achieve this, the study proposes a deep learning method that uses long short-term memory (LSTM) artificial neural networks. The proposed model is an ensemble of LSTM networks that utilize relevant indoor environmental data from multiple sensory systems and weather data from a nearby weather station to predict the window state with different predictive horizons. The drawbacks of such networks are extensive training time and the lack of interpretability. Hence, Bayesian optimization is proposed for efficient hyperparameter tuning of the network. Furthermore, explainability through a model agnostic approach is utilized for feature importance ranking.The performance of the LSTM networks was compared to a dense feedforward neural network (DFNN) on an imbalanced dataset to evaluate the effect of the long-term past. Handling imbalanced window operation data was improved by employing class weight and early stopping, leading to a higher true positive rate, marginally better F1-score, and fewer network training epochs. The results indicate that a stateful bi-directional LSTM (Bi-LSTM) network models the data sequence more efficiently in aggregated comparison than other LSTM types and DFNN. However, the DFNN slightly outperformed LSTM networks in interval-by-interval comparison, with an F1-score of 0.88 when predicting the window state 10 min ahead, shifting to 0.82 for a 60-minute predictive horizon. Additionally, the study showed that the ensemble model performed better in terms of F1-score than the individual models.