A Collection of 85 Datasets of Buildings and Building Clusters Performing Demand Response – Common Exercise IEA EBC Annex 82
Bibliographic record
Abstract
This open-access dataset contains 85 simulation datasets of buildings and clusters of buildings performing demand response. These datasets have been generated within the framework of a common exercise of the IEA EBC Annex 82 project: Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems (https://annex82.iea-ebc.org/). Each dataset is placed inside a dedicated folder with a corresponding case ID. Each dataset contains a metadata file and one or several nomenclature files providing information about the content of the dataset and the characteristics of the case building cluster, and how this data has been generated. The datasets contain time series of building- and energy-related monitoring variables (e.g., total power demand, grid incentive signal, outdoor temperature, etc.) for reference (ref) scenarios (no demand response activations) and flexible (flex) scenarios (demand response activations). These datasets are analyzed in a dedicated scientific publication. More information can be found in the “Dataset_description” file. The Python file "dataset_treatment_and_analysis.py" is a script that runs a validity verification of the entire collection of datasets and, if all datasets are valid, generates a series of plots with the data from the different datasets. This Python script can be used as a basis to develop further data treatment and analysis processes. For questions and comments, please contact Hicham Johra: hicham.johra@sintef.no
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.021 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".