Efficient built environment control is essential for balancing energy consumption, thermal comfort, and indoor air quality (IAQ), especially in spaces with highly dynamic and intermittent occupancy patterns. Traditional control strategies, such as fixed schedules or simple occupancy-based rules, often fail to address the stochastic nature of occupancy behaviors, leading to suboptimal performance. This study proposes a stochastic occupancy-integrated model predictive control (MPC) strategy that advances built environment optimization through several innovative contributions. First, the proposed MPC integrates stochastic occupancy number predictions into its control scheme, enabling multi-objective optimization considering thermal comfort and IAQ for spaces with sudden occupancy changes and irregular usage. Second, the stochastic differential equations (SDE)-based building dynamic models are developed considering the stochasticity and time-inhomogeneity of occupancy heat gains and CO₂ generations in the prediction of indoor temperature, CO₂ concentration and energy consumption. Third, a TRNSYS-Python co-simulation platform is established to evaluate the MPC strategy’s performance, addressing the discrepancies between the SDE models used for MPC and the actual process of the target system. Finally, the study comprehensively evaluates the MPC’s multi-dimensional performance under different optimization weight combinations and benchmarks it against two baseline strategies: a fixed-schedule (FIX) strategy and occupancy-based control (OBC) strategies with varying per-person fresh airflow rates. Simulation results demonstrate that the proposed MPC achieves 32% energy savings and 17% IAQ improvement compared to the FIX strategy, and 30% thermal comfort improvement and 20% IAQ improvement with the same energy consumption compared to OBC. These findings highlight the robustness and enhanced performance of the proposed MPC in addressing the complexities of stochastic and time-varying occupancy, offering a state-of-the-art solution for energy-efficient and occupant-centric built environment control.

Buildings have a significant impact on global sustainability. During the past decades, a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance. Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications. Recent advances in information technologies and data science have enabled convenient access, storage, and analysis of massive on-site measurements, bringing about a new big-data-driven research paradigm. This paper presents a critical review of data-driven methods, particularly those methods based on larger datasets, for building energy modeling and their practical applications for improving building performances. This paper is organized based on the four essential phases of big-data-driven modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have been summarized and compared at each stage, based upon which in-depth discussions and future research directions have been presented. This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling. Furthermore, considering the ever-increasing development of smart buildings and IoT-driven smart cities, the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

Accurate building energy prediction is vital to develop optimal control strategies to enhance building energy efficiency and energy flexibility. In recent years, the data-driven approach based on machine learning algorithms has been widely adopted for building energy prediction due to the availability of massive data in building automation systems (BASs), which automatically collect and store real-time building operational data. For new buildings and most existing buildings without installing advanced BASs, there is a lack of sufficient data to train data-driven predictive models. Transfer learning is a promising method to develop accurate and reliable data-driven building energy prediction models with limited training data by taking advantage of the rich data/knowledge obtained from other buildings. Few studies focused on the influences of source building datasets, pre-training data volume, and training data volume on the performance of the transfer learning method. The present study aims to develop a transfer learning-based ANN model for one-hour ahead building energy prediction to fill this research gap. Around 400 non-residential buildings’ data from the open-source Building Genome Project are used to test the proposed method. Extensive analysis demonstrates that transfer learning can effectively improve the accuracy of BPNN-based building energy models for information-poor buildings with very limited training data. The most influential building features which influence the effectiveness of transfer learning are found to be building usage and industry. The research outcomes can provide guidance for implementation of transfer learning, especially in selecting appropriate source buildings and datasets for developing accurate building energy prediction models.