Buildingenergy.ninja: A web-based surrogate model for instant building energy time series for any climate.
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
Abstract Machine learning-based surrogate models are trained on building energy simulation input and output data. Their key advantage is their computational speed allowing them to produce building performance estimates in fractions of a second. In this work we showcase the use of deep convolutional neural network surrogate models embedded into a web application, allowing users to rapidly explore building performance at high spatio-temporal resolution. Users can pick any climate on an interactive map, customize a building design with thirteen decisive design parameters, and the surrogate model allows them to retrieve hourly heating and cooling load time series data in fractions of a second. In this work, we further show that the surrogate model reaches an accuracy of R 2 > 0.93 ( MAE < 0.27 kWh) for unseen design specifications and climates. These results motivate the use of computationally cheap surrogate models to replace building energy simulation for a wide variety of tasks in the future.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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