Who gains from hourly time‐of‐use retail prices on electricity? An analysis of consumption profiles for categories of Danish electricity customers
Bibliographic record
Abstract
Studies of the aggregated hourly electricity load in geographical areas typically show a systematic variation over the day, the week, and seasons. With hourly metering of individual customers, data for individual consumption profiles have become available. Looking into these data we show that consumption profiles for specific categories of customers are equally systematic but quite distinct for different categories of customers. That is, different categories of customers contribute quite differently to the aggregated load profile. Coupling consumption profiles with hourly market prices which also include a systematic component in the hourly variation, we show that customers with different consumption profiles experience different average cost of their electricity consumption when billed according to hourly time‐of‐use prices. Thus, some categories of customers stand to gain from time‐of‐use pricing, while others stand to lose. In Denmark, typically industry, private services and households stand to lose, whereas agriculture and public services stand to gain from time‐of‐use pricing. However, differences within categories of customers are considerable and, for example, industrial companies running 24 h a day tend to gain from a time‐of‐use pricing. WIREs Energy Environ 2014, 3:582–593. doi: 10.1002/wene.120 This article is categorized under: Energy Infrastructure > Economics and Policy Energy Policy and Planning > Economics and Policy
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".