A reproducible method to generate multi-building, multi-climate HVAC operation datasets with a stochastic exploratory controller
Building control research increasingly requires datasets that are reproducible, controllable, and rich in action–state coverage. We present a method to generate multi-year HVAC operation time series across heterogeneous buildings and climates using open-source building simulation frameworks, which e...
| Autores: | , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat de Lleida (UdL) |
| Repositorio: | Repositori Obert UdL |
| OAI Identifier: | oai:dnet:.___________::a55454086a882e9d185843ebef664e20 |
| Acceso en línea: | https://doi.org/10.1016/j.mex.2026.103866 https://hdl.handle.net/10459.1/469956 |
| Access Level: | acceso abierto |
| Palabra clave: | HVAC Building energy systems Dataset generation method |
| Sumario: | Building control research increasingly requires datasets that are reproducible, controllable, and rich in action–state coverage. We present a method to generate multi-year HVAC operation time series across heterogeneous buildings and climates using open-source building simulation frameworks, which expands control diversity using a deliberately stochastic supervisory controller. The workflow combines EnergyPlus-based simulation via Sinergym for multi-building/multi-climate “source” domains and Modelica-based simulation via BOPTEST for a distinct “target” domain to support transfer-learning evaluation and reproducible comparisons. Alongside the default rule-based controller (RBC), we implement a stochastic exploratory policy that interleaves stochastic drift, ramps, oscillations, jumps, and noisy holds to produce non-routine heating/cooling setpoint trajectories under operational bounds. The method produces standardized 15-minute multivariate time series including indoor temperature, outdoor weather, setpoints, and HVAC power, and releases both the datasets and the full code needed to reproduce or extend them. •Reproducible pipeline combining Sinergym (EnergyPlus) and BOPTEST (Modelica) under a common interface. •Stochastic HVAC supervisor that broadens setpoint distributions beyond standard schedules. •FAIR release of code + datasets to enable evaluation and reproducible comparisons, transfer learning, and robustness studies. |
|---|