A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research

2025-11-29

AI Summary

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.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Data-driven modeling of building thermal dynamics is emerging as an increasingly important field of research for large-scale intelligent building control. However, research in data-driven modeling using machine learning (ML) techniques requires massive amounts of thermal building data, which is not easily available. Neither empirical public datasets nor existing data generators meet the needs of ML research in terms of data quality and quantity. Moreover, existing data generation approaches typically require expert knowledge in building simulation. To fill this gap, we present a thermal building data generation framework which we call BuilDa. BuilDa is designed to produce synthetic data of adequate quality and quantity for ML research. The framework does not require profound building simulation knowledge to generate large volumes of data. BuilDa uses a single-zone Modelica model that is exported as a Functional Mock-up Unit (FMU) and simulated in Python. We demonstrate BuilDa by generating data and utilizing it for a transfer learning study involving the fine-tuning of 486 data-driven models.

Problem

• Generates synthetic thermal building data for machine learning research
• Addresses lack of quality and quantity in existing building thermal datasets
• Enables data creation without deep building simulation expertise

Innovation

• Configurable framework for large-scale thermal data generation
• Uses Modelica FMU exported to Python for simulation
• Generates synthetic data without requiring expert simulation knowledge