Exploring attitudes and behavioral patterns in residential energy consumption: Data-driven by a machine learning approach
Bibliographic record
Abstract
The present study focuses on two main objectives: firstly, to clarify the mechanisms by which attitudes impact behavioral changes related to household energy consumption, and secondly, to offer valuable insights to enhance the understanding of residential energy usage through a novel technique called Support Vector Regression (SVR). This method employs several feature space transformations to convert nNar relationships into linear ones. The results highlight the crucial role of psychological factors in determining energy consumption behaviors, demonstrating that cognitive factors significantly influence attitudes and behavioral patterns. The findings show that psychological variables have a major role in determining how people consume energy, with cognitive variables having a particularly large impact on attitudes and behavior patterns. Our findings demonstrate the superior performance of Support Vector Regression (SVR) with radial basis function kernels over traditional predictive models , with a prediction accuracy of 93.7 % for changes in behavior patterns (CHP) and 94.4 % for changes in attitudes (CHA). These results highlight the value of applying cutting-edge machine-learning approaches to create precise models for comprehending and directing energy-saving actions. The policy implications suggest that reducing cognitive barriers can significantly encourage energy-saving behaviors and contribute to a comprehensive approach for energy-efficiency initiatives
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".