Inverse surrogate modelling to determine thermal characteristics of buildings
Bibliographic record
Abstract
Planning building energy retrofits effectively requires knowledge of the current state of the building envelope, which is often lacking in practice. This study examines the usage of an Inverse Surrogate Model (ISM) for the purposes of determining various building parameters, such as the wall insulation conductivity and infiltration flow rate, to assist in retrofit planning. A typical Surrogate Model (SM) is a machine learning model trained on detailed simulation inputs to predict outputs, so that it can be used as a fast but approximate substitute for the detailed model. An Inverse Surrogate Model (ISM) does the opposite by instead predicting which inputs were used in the detailed model (e.g. wall insulation thickness) to produce a specific set of outputs (e.g. a temperature time series). This study develops a convolutional neural network (CNN) to act as the ISM, as these have been shown to work well with time-domain problems.An EnergyPlus simulation model of an existing building was developed and various parameters were randomly varied to produce a training dataset consisting of parameter values and associated room temperature time series. The CNN was trained using this dataset to predict parameter values when given a time series as input. The ISM performance is assessed with a variety of common error metrics including the Coefficient of Determination (R2), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Results indicated strong performance with the majority of parameters.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".