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

TL;DR

This paper introduces BuilDa, a flexible framework for generating large-scale, high-quality synthetic thermal building data suitable for machine learning research, without requiring expert simulation knowledge.

Contribution

BuilDa provides an accessible, scalable data generation tool for ML in building thermal modeling, filling gaps left by existing datasets and simulation approaches.

Findings

Generated data enabled effective transfer learning for thermal models

BuilDa produces data comparable to real-world datasets

Framework simplifies large-scale data creation for ML 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.