A Conditional Generative adversarial Network for energy use in multiple buildings using scarce data
Bibliographic record
Abstract
Building consumption data is integral to numerous applications including retrofit analysis, Smart Grid integration and optimization, and load forecasting. Still, due to technical limitations, privacy concerns and the proprietary nature of the industry, usable data is often unavailable for research and development. Generative adversarial networks (GANs) - which generate synthetic instances that resemble those from an original training dataset - have been proposed to help address this issue. Previous studies use GANs to generate building sequence data, but the models are not typically designed for time series problems, they often require relatively large amounts of input data (at least 20,000 sequences) and it is unclear whether they correctly capture the temporal behaviour of the buildings. In this work we implement a conditional temporal GAN that addresses these issues, and we show that it exhibits state-of-the-art performance on small datasets. 22 different experiments that vary according to their data inputs are benchmarked using Jensen-Shannon divergence (JSD) and predictive forecasting validation error. Of these, the best performing is also evaluated using a curated set of metrics that extends those of previous work to include PCA, deep-learning based forecasting and measurements of trend and seasonality. Two case studies are included: one for residential and one for commercial buildings. The model achieves a JSD of 0.012 on the former data and 0.037 on the latter, using only 396 and 156 original load sequences, respectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".