Exploring the power of machine learning to drive energy efficiency in Halifax, West Yorkshire, England: A predictive energy efficiency model for sustainable and resilient buildings and households
Bibliographic record
Abstract
Population growth and urbanization have increased building energy demand over the past few decades, which has become linked to environmental issues like climate change, air pollution, and thermal imbalances, which have serious health consequences. Halifax, West Yorkshire, has many homes with energy ratings of D and E, which increases CO2 emissions and depletes energy resources. This study examines energy efficiency in buildings by studying climatic, energy usage, and structural elements that affect energy ratings. The effective analysis and administration of this region remain unknown despite earlier studies. This research develops and assesses six machine learning classification models—SVM, RF, GB, XGBoost, KNN, and ET—to forecast energy ratings in the UK's Energy Performance Certificate (EPC) standard rating scale. Model parameters are optimized, important aspects are prioritized, and computational efficiency is being assessed.Sensitivity and correlation analysis illuminate key factors. Ensemble learning can accurately estimate energy performance, which is promising. This study improves Halifax's building energy efficiency image by suggesting greener energy 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".