Sang woo Ham | Searcharxiv

arXiv2026

Tensorized Radiative Heat Transfer for a Scalable and Calibrated Building Energy Simulator

Accurate building energy simulation is essential for developing advanced control strategies that enable demand flexibility and grid responsiveness. The Smart Buildings Control Suite (sbsim) offers a lightweight, scalable, and data-calibrated simulation environment based on a tensorized finite difference model. Previous work extended sbsim to include interior long-wave radiative heat exchange between indoor surfaces. However, a complete thermal model must also account for exterior radiative processes, including long-wave radiation exchange with the sky and surroundings, as well as short-wave solar radiation incident on building surfaces. This paper presents a comprehensive radiative heat transfer implementation for sbsim that integrates both interior and exterior radiation mechanisms. Our primary contribution is the development and integration of a fully tensorized exterior radiation module that captures sky and ground long-wave exchange as well as solar heat gains through opaque and transparent surfaces. By formulating these processes as tensor operations compatible with the existing framework, we preserve the computational efficiency necessary for reinforcement learning applications. We validate our implementation against established simulation tools and demonstrate improved prediction accuracy for surface temperatures and building thermal loads. This enhancement significantly increases the physical fidelity of sbsim, enabling more realistic training environments for building energy optimization and control.

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arXiv2025

Hybrid modeling approach for better identification of building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this study, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The Hybrid model approach demonstrates the reduction of RMSE approximately 0.2-0.9C and 0.3-2C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied for experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

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