A neural network-based surrogate model to predict building features from heating and cooling load signatures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Addressing the challenges of scalable and cost-effective energy performance analysis in mid to high-rise office buildings, this paper introduces a novel approach utilizing an inverse-based artificial neural network (ANN). This ANN was trained on synthetically generated heating and cooling load parameters – derived from simulations conducted in EnergyPlus – to predict characterization parameters, including the building envelope, internal heat gains, and HVAC operational parameters. Diverging from traditional forward surrogate models, this inverse surrogate model fills a critical gap in current building energy modeling approaches that are hindered by data and resource limitations. Its effectiveness is verified with a testing dataset of 3000 buildings and is further demonstrated through a case study in Ottawa, Ontario. Proving to be an efficient, cost-effective tool for energy retrofit screening, the model is enhanced by a user-friendly web-based application (Ferreira and Gunay), marking a significant advancement in accessible building energy analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it