Optimizing Energy Consumption in Buildings: Intelligent Power Management Through Machine Learning
Bibliographic record
Abstract
In the realm of energy conservation, managing power consumption within buildings emerges as a pivotal challenge. This study introduces sophisticated models that optimize energy usage by intelligently managing power distribution in various zones of a building. To achieve this, four machine learning classifiers, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) algorithm, and Naive Bayes (NB), were employed. These classifiers were integrated with feature reduction techniques, namely Boruta and Principal Component Analysis (PCA), to diminish model complexity. The study delineates three distinct power management strategies: Full, Selected, and Shutdown. The effectiveness of these models was evaluated using a dataset obtained from a building's energy consumption measurements. A comparative analysis revealed that the integration of the RF classifier with the Boruta feature reduction method significantly excelled, achieving a classification accuracy of 98%. Additionally, this combination demonstrated an execution time of merely 0.4549 seconds. The findings of this research not only underscore the efficacy of combining specific machine learning classifiers with feature reduction techniques but also highlight the potential of such integrations in optimizing energy consumption in building environments. This approach paves the way for more energy-efficient and sustainable building management practices.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".