Examining the generalizability of inverse surrogate models for different geometries and locations
Bibliographic record
Abstract
• Investigates generalizability of inverse surrogate models (ISMs). • Compares CNN, RNN, and transformer architectures in building energy modeling. • Uses internal temperature and energy data as time-series inputs. • Demonstrates ISM applicability across diverse building shapes and locations. • Highlights transformers’ strengths in sequence learning for predictive modeling. While building surrogate modelling has been shown to accurately replicate the outputs of computationally intensive building energy modelling, successfully adopting surrogate modelling in practice still has challenges. As surrogate models are machine learning models, they require an extensive quantity of training data in order to train effectively. The process of acquiring training data often requires numerous simulation runs of a building energy model. To offset this issue, surrogate models that demonstrate a suitable level of generalizability can be applied successfully to multiple projects without the need for the further generation of data. This study examines the generalizability of multiple inverse surrogate models. Inverse surrogate modelling is a more difficult task than traditional surrogate modelling as it tries to extract building energy model inputs from output data. As the output data required to do this is often comprehensive, deep learning models are preferred. For the inverse surrogate models, a basic deep artificial neural network, convolutional neural network, recurrent neural network and transformer were examined. Output data in this study consisted primarily of temperature and energy time series data with input data being building energy model parameters reflective of thermally important building characteristics. Generalizability is assessed by first training the inverse surrogate models on data from 3 separate building energy models. Each of the building energy models contain geometry that is randomly scaled. Additionally we examine training the inverse surrogate models on building energy model data produced with multiple locations as well as on data from all building energy models at once. Parameters relating to the building envelope demonstrated the highest prediction performance among the models, whereas the prediction performance for less influential parameters was more varied depending on the inverse surrogate model. Overall, the convolutional neural network typically outperformed the other models with the recurrent neural network and transformer producing slightly worse performance. The artificial neural network was unable to accurately predict parameters outside of a select few that were highly influential to the time-series data. In the cases of training with data from multiple locations or all buildings at once, prediction performance decreased, however several parameters remained predictable.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".