Coupling of neural models for predicting indoor temperatures and heating loads in buildings
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".