Description
The archive provides all year-2023 hourly data required to reproduce the results:
* District heating (DH)
* Domestic hot water (DHW)
* Air-handling unit (AHU) fan electricity
* Space heating (SH)
* Floor heating (FH)
* Outdoor temperature
These measurements support the construction of the residual target rt+r_t^{+}rt+, weak labels, and subsystem-specific training/validation used in the paper.
The package also includes:
* Preprocessing scripts and notebooks (data alignment, quality control, residual target, and weekly structure checks)
* Weak labeling pipeline for DHW and AHU, including confidence weights
* Model training and statistical validation notebooks
* Regression, identifiability, and separation metrics
* Code to reproduce all figures in Section 3.6 (e.g., R² plots, mutual information, distributional distances, Δ-metrics)
All materials are provided under the Creative Commons Attribution 4.0 International (CC-BY-4.0) license and may be reused with proper attribution.
This dataset enables full transparency and reproducibility of the proposed weakly supervised heating-component inference framework and serves as a reference benchmark for future research in data-driven building-energy analytics.
* District heating (DH)
* Domestic hot water (DHW)
* Air-handling unit (AHU) fan electricity
* Space heating (SH)
* Floor heating (FH)
* Outdoor temperature
These measurements support the construction of the residual target rt+r_t^{+}rt+, weak labels, and subsystem-specific training/validation used in the paper.
The package also includes:
* Preprocessing scripts and notebooks (data alignment, quality control, residual target, and weekly structure checks)
* Weak labeling pipeline for DHW and AHU, including confidence weights
* Model training and statistical validation notebooks
* Regression, identifiability, and separation metrics
* Code to reproduce all figures in Section 3.6 (e.g., R² plots, mutual information, distributional distances, Δ-metrics)
All materials are provided under the Creative Commons Attribution 4.0 International (CC-BY-4.0) license and may be reused with proper attribution.
This dataset enables full transparency and reproducibility of the proposed weakly supervised heating-component inference framework and serves as a reference benchmark for future research in data-driven building-energy analytics.
| Date made available | 18 Nov 2025 |
|---|---|
| Publisher | Zenodo |
Dataset Licences
- CC-BY-4.0
Cite this
- DataSetCite