Computational BIM and Intelligent Operations
Temporal-online hybrid machine learning for occupancy forecasting: Enhancing energy efficiency in smart building management systems
Abstract
Enhancing energy efficiency is critical for reducing the carbon footprint of the built environment, with accurate building occupancy prediction serving as a foundation for smart energy management. Traditional occupancy forecasting models often suffer from poor adaptability to sudden changes, such as holidays, and are limited by computational intensity, hindering their real-world scalability. To address these limitations, we propose a novel approach integrating a robust machine learning model with a dynamic re-training framework. We first benchmarked several models using high-frequency (1-minute interval) occupancy data from a meeting room, including simpler methods (e.g., Random Forest, Support Vector Regression) and advanced time-series techniques (e.g., Long Short-Term Memory, Temporal Convolutional Networks). The Random Forest (RF) model was selected for its superior balance of prediction performance and computational efficiency. To ensure robustness and adaptability, the RF model was augmented with time-based, error-based, and event-based online learning mechanisms. This integration allows the model to dynamically adapt to occupancy fluctuations and abrupt changes, such as holiday periods. This combined online RF model achieved a prediction accuracy of 0.92, representing an 80% improvement over the base RF model’s performance in handling abrupt events. These findings demonstrate the significant potential of integrating advanced machine learning with dynamic re-training to create highly adaptive, accurate, and energy-efficient building management systems.
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Cite this paper
@article{6f068d1a,
title = {Temporal-online hybrid machine learning for occupancy forecasting: Enhancing energy efficiency in smart building management systems},
journal = {Energy and Buildings},
pages = {116736},
year = {2025},
issn = {0378-7788},
doi = {https://doi.org/10.1016/j.enbuild.2025.116736},
url = {https://www.sciencedirect.com/science/article/pii/S0378778825014665},
author = {Ruosi Zhang and Oleksiy V. Klymenko and Michael Short},
keywords = {Machine Learning, Online Learning, Smart Buildings, Building Occupancy Prediction, Computational Efficiency},
abstract = {Enhancing energy efficiency is critical for reducing the carbon footprint of the built environment, with accurate building occupancy prediction serving as a foundation for smart energy management. Traditional occupancy forecasting models often suffer from poor adaptability to sudden changes, such as holidays, and are limited by computational intensity, hindering their real-world scalability. To address these limitations, we propose a novel approach integrating a robust machine learning model with a dynamic re-training framework. We first benchmarked several models using high-frequency (1-minute interval) occupancy data from a meeting room, including simpler methods (e.g., Random Forest, Support Vector Regression) and advanced time-series techniques (e.g., Long Short-Term Memory, Temporal Convolutional Networks). The Random Forest (RF) model was selected for its superior balance of prediction performance and computational efficiency. To ensure robustness and adaptability, the RF model was augmented with time-based, error-based, and event-based online learning mechanisms. This integration allows the model to dynamically adapt to occupancy fluctuations and abrupt changes, such as holiday periods. This combined online RF model achieved a prediction accuracy R2 of 0.92, representing an 80% improvement over the base RF model’s performance in handling abrupt events. These findings demonstrate the significant potential of integrating advanced machine learning with dynamic re-training to create highly adaptive, accurate, and energy-efficient building management systems.}
}