Environmental Assessment of Fluctuating Residential Electricity Demand
Bibliographic record
Abstract
Including dynamic aspects in the environmental assessment of power systems allows computing the environmental benefits of demand-side management strategies for the smart grid which could not be assessed with static data such as shifting part of the demand from one period to another. Several methodological approaches have been developed in life cycle assessment to account for dynamic aspects, but none has given much attention to the demand side of the equation. However, demand is also prone to fluctuate in time and its misrepresentation may lead to additional errors. In this study, a stochastic approach was applied to model the fluctuating residential power demand of Canadians' homes. An hourly and a yearly average electricity mix were then used to compute the environmental impacts of the hourly or yearly average homes' electricity demand. Finally, an approach combining an average and a marginal hourly electricity mix was then proposed to assess the benefits of a simple demand side management strategy: the shifting of homes' dryers loads up to two hours later than usual. Results show that assuming a constant demand or electricity mix both leads to errors which may be as high as 150% depending on the period of the month assessed. Moreover, using an hourly average electricity mix to set up the demand side strategy increases climate change impact by 0.6% whereas using a marginal mix decreases climate change impact by 10%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".