Innovative Lighting Systems: Opportunities for Energy Savings
Bibliographic record
Abstract
This paper analyses, from an energy flow perspective, the implementation of smart lighting systems in street lighting, where lights are dimmed to adapt to the flow of objects passing in a street. The research focus on the sustainability perspective of implementing a transition to smart lighting systems when compared to regular LED lighting. To account for externalities, the energy flow was addressed considering the extra electronic devices used in a smart lighting system (controllers, motion sensors, radars, and computers). To compare both traditional LED street lighting and smart lighting the paper started with a model of a 2.5-kilometre street, scaling up scenarios of the commune Ecublens, in the Swiss canton of Vaud, and then to half and all residential streets of Switzerland were examined to understand if the gains in energy savings are scalable. The research shows that, even with the additional electronic devices, the smart lighting system reduces the energy consumption of street lighting, even when considering the production of the extra components used. Financially, the extra costs of implementing smart lighting systems are offset by the savings in electricity consumption. Therefore, smart lighting systems for street lighting can be an environmentally and economically beneficial project to implement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".