A hybrid machine learning approach integrating recurrent neural networks with subspace identification for modelling <scp>HVAC</scp> systems
Bibliographic record
Abstract
Abstract This paper addresses the problem of system identification for heating, ventilation, and air conditioning (HVAC) systems using a relatively small amount of data for the zone under consideration, by leveraging larger datasets for similar zones. To this end, a hybrid machine learning approach is developed where a pre‐trained recurrent neural network (RNN) model, trained on a large amount of data from a representative zone, is leveraged to build models for the other zones using a smaller amount of data. This is achieved by developing a hybrid model that integrates the pre‐trained RNN model with the models built using the subspace identification (SubID) technique to predict the residuals (differences between the real outputs and the predicted outputs from the pre‐trained RNN model) in the other zones. The effectiveness of the proposed hybrid approach is shown using real data collected from a multi‐zone fitness centre. The results demonstrate the superior performance of the hybrid approach over the cases where individual RNN and SubID models are directly developed using only the data from the zones in question.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".