Modeling Domestic Lighting Energy Consumption in Romania and Integrating Consumers’ Behavior
Bibliographic record
Abstract
Sustainable energy consumption is a research area of a large interest in the last years. New energy-efficient lighting technologies exist, and can significantly reduce household electricity consumption, but their adoption has been slow. A considerable number of international studies show that sustainable development scenarios will be realistic if they involve the human behavior. The paper provides a solution to integrate consumer behavior with techno-economic energy issues, inside a complex energy model. This approach aims to reduce the systematic error on the demand side of the energy model that stems from the hypotheses of perfect information and of perfect economic rationality. These hypotheses are common to all optimization models based on the concept of economic equilibria. The TIMES energy modeling software tool has been used to integrate the techno-economic energy specific data and consumer behavior. Consumer behavior in energy consumption is described using specific attributes of energy technologies as virtual technologies. Technical coefficients of virtual technologies come from a sociological survey about Romanians’ behavior in energy consumption. This approach, known as “Social MARKAL”, allows the analyst to evaluate the possible contribution of information campaigns in changing lighting consumers’ behavior and effect of technology switch. The model is developed for the period 2010-2026, and is implemented by using TIMES/VEDA software platform. The implementation presented this paper focuses on the household lighting technologies but the approach can be extended to other demand sectors as well.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".