building energy modeling

Existing building thermal datasets lack sufficient diversity in building types and operational conditions, limiting advances in thermal dynamic modeling, energy efficiency control, and fault diagnosis. This work addresses this gap by constructing and releasing the ThermBuild dataset, which uniquely integrates 15 months of high-resolution (15-minute) measured data from two real residential buildings with three years of TRNSYS-simulated data from 958 residential units. The dataset encompasses diverse heat pump systems, building characteristics, and climatic conditions, and includes multidimensional variables such as heat pump operation, indoor environmental parameters, and weather data. ThermBuild enables research in cross-domain transfer learning, simulation-to-reality generalization, and model benchmarking, significantly enhancing the applicability, robustness, and reproducibility of data-driven approaches in building energy systems.

This work addresses the limitation of current large language models (LLMs) in multi-zone HVAC control, which typically lack explicit modeling of building physics and thermodynamic processes. To bridge this gap, the authors propose a knowledge graph that integrates thermodynamic principles with spatial semantics, constructed upon the Brick ontology and enriched with historical environment-controller interaction data to provide LLMs with structured contextual information. This approach represents the first integration of physics-informed spatial semantic graphs into an LLM-based control framework, explicitly capturing inter-zone thermal couplings and building dynamic responses. Evaluated in a five-zone building simulation, the method significantly improves the trade-off between energy efficiency and occupant comfort compared to both conventional and existing LLM-based strategies, achieving the lowest PMV violation rate while maintaining high energy performance.

High-quality, large-scale empirical data are scarce in machine learning research on building thermal dynamics. Method: This paper proposes a low-barrier, scalable synthetic data generation framework that integrates a Modelica-based single-zone thermal model with Functional Mock-up Unit (FMU) export capabilities, enabling fully automated Python-driven simulations without requiring domain expertise in building simulation. Contribution/Results: The framework significantly enhances data scale and configuration flexibility compared to existing tools. It generates a large-scale dataset suitable for transfer learning and validates fine-tuning across 486 data-driven models. Experimental results demonstrate superior effectiveness, generalizability, and scalability, establishing a robust data infrastructure for AI-driven building research.

Existing building thermal dynamics datasets are predominantly derived from steady-state operation under fixed control policies, which inadequately excite the system’s state space and consequently limit the generalization capability of data-driven models. To address this, this work proposes BuilDyn, a novel toolkit that introduces, for the first time, a control-oriented active excitation mechanism. By combining sampling across building parameter distributions with customizable excitation strategies, BuilDyn generates thermal dynamics data with high state-space coverage and integrates seamlessly into machine learning workflows via a Python API. Experimental results demonstrate that models trained on BuilDyn-generated data significantly outperform those based on conventional datasets in both predictive accuracy and generalization, thereby establishing a robust data foundation for control-oriented modeling, transfer learning, and the development of building-specific foundation models.

Existing building thermodynamic models rely heavily on long-term historical data and domain expertise, resulting in poor generalizability and limited transferability—hindering their applicability to real-time HVAC control. To address this, we propose a thermodynamic model integration framework specifically designed for HVAC control. Our approach introduces a novel hierarchical reinforcement learning (HRL)-based mechanism for dynamic model selection and online weighted ensemble, enabling adaptive modeling of non-stationary building time-series data. Leveraging pre-existing models as foundational components, the framework eliminates the need for de novo modeling, thereby significantly enhancing cross-building transferability and modeling efficiency. Offline evaluations and on-site deployment demonstrate that our method reduces prediction error by 32%, decreases modeling time by 76%, and achieves a 14.8% energy saving in HVAC operation.

This work addresses the lack of a generalizable thermal dynamics model in building energy systems that can transfer across diverse buildings, climates, and control strategies. The authors propose a decoder-only Transformer architecture infused with physical priors, embedding thermodynamic laws through derivative augmentation and Euler-based numerical integration. By integrating static building characteristics and rotary positional encoding (RoPE), the model achieves universal representation capabilities. Notably, this is the first approach to incorporate physics-informed constraints directly into a Transformer framework, yielding a foundational thermal model capable of zero-shot transfer. Evaluated on the CityLearn dataset, the model achieves single-step prediction RMSE as low as 0.29–0.30°C after training on only two buildings, and demonstrates strong generalization to unseen buildings and climate zones, significantly outperforming both conventional methods and fine-tuned temporal foundation models.

This work addresses the limitation in current building energy modeling caused by the absence of large-scale datasets that explicitly link geometric, topological, and physical properties. The authors introduce ArchEGraph, the first large-scale graph-based building energy dataset that aligns geometry, topology, and physics by representing buildings as heterogeneous graphs incorporating spaces, surfaces, weather conditions, and thermal loads. The dataset enables two benchmark tasks: graph reconstruction and topology-aware load prediction. ArchEGraph encompasses 5,481 buildings, 49,326 simulation cases, 133,000 space nodes, and 1.44 million surface nodes, supporting generalization evaluations across buildings and climates. It facilitates research on scalable surrogate models, and its standardized protocols validate both the effectiveness of the proposed tasks and the robustness of evaluated models.

Accurately forecasting building-level short-term thermal loads is crucial for enhancing the efficiency of district heating systems, yet it is significantly influenced by exogenous factors such as outdoor temperature and occupant behavior. This study leverages hourly data from 25 buildings in Germany spanning 2017–2025 to systematically compare, for the first time in a multi-building setting, the performance, cross-building generalization capability, and computational overhead of xLSTM, Temporal Fusion Transformer (TFT), and fully connected networks across 3-hour and 24-hour forecasting horizons. Results show that xLSTM achieves the lowest RMSE (3h: 19.88 kWh; 24h: 21.47 kWh), while TFT yields the best MAE (3h: 9.16 kWh). Notably, lightweight models attain comparable accuracy, revealing a pronounced imbalance between the marginal gains of high-parameter models and their associated computational costs.