A Machine Learning-Driven HVAC Optimization: Occupancy Prediction and Detection at Scale
Bibliographic record
Abstract
Optimizing the operation of heating, ventilation, and air Conditioning (HVAC) systems is crucial for improving energy efficiency, enhancing user comfort, and maximizing system performance. This paper proposes a machine learning (ML)-based method that leverages environmental sensor data to predict high room occupancy, enabling more efficient HVAC operation. It integrates multiple ML techniques to enhance energy efficiency by minimizing HVAC operation in unoccupied spaces while maintaining occupant comfort. The methodology involves using sensor readings, such as temperature, humidity, light, and CO2levels to improve occupancy detection accuracy. It exploits advanced techniques such as synthetic minority over-sampling technique (SMOTE)-Tomek for data balancing, eXtreme Gradient Boosting for robust classification, multi-layer artificial neural networks (ML-ANN) for capturing nonlinear relationships, and long short-term memory (LSTM) models for time-series forecasting. To further improve prediction accuracy, a probabilistic voting classifier is implemented by combining the predictions of the above individual models, leveraging a soft voting mechanism to generate more reliable occupancy forecasts. The results show that the proposed solution achieves highly competitive performance on a publicly available dataset. Index Terms-Ensemble learning, HVAC, machine learning, time series analysis.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".