Coupling of neural models for predicting indoor temperatures and heating loads in buildings
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
Building energy models are critical to forecast the energy use and to improve the operations of HVAC systems. However, these models are building-specific, and their development is tedious, error-prone and time-consuming. Compared to traditional white-box and grey-box models, black-box models need less development time and no information about the building properties, and only rely on collected data. In this work, a model coupling two neural networks is developed and used to simulate the building energy behaviour: both networks predict successively the indoor temperatures and heating loads of each room. The model is trained, validated and compared to experimental data obtained for seven houses in Canada heated by electric baseboards controlled by connected thermostats (on average, ten thermostats per house). For simulations with a time horizon of two days and a timestep of one hour, errors are promising, especially in winter where root mean square errors are up to 0.29 °C for indoor temperatures and 1050 Wh for heating loads. In summer, errors are higher due to the free-floating nature of simulations, with root mean square errors up to 1.09 °C for indoor temperatures and 139 Wh for heating loads.
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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