Romanian election panel survey 2016
Bibliographic record
Abstract
To efficiently facilitate the energy transition it is essential to evaluate the potential of demand response in practice. Based on the results of a Dutch smart grid pilot, this paper assesses the potential of both manual and semi-automated demand response in residential areas. To stimulate demand response, a dynamic tariff and smart appliances were used. The participating households were informed about the tariff day-ahead through a home energy management system, connected to a display installed on the wall in their living room. The tariff was intuitively displayed: self-consumption of photovoltaic generation was stimulated by means of a low tariff, but also the generation itself played a central role on the display. Household flexibility is analyzed, focusing on: (i) the load shift of (smart) appliances, and (ii) the response of the (overall) peak load towards the dynamic tariff. To assess the latter, i.e. price responsiveness, the participants were split up in two comparable groups which were subject to a different moment of evening peak-pricing. Based on the results, it is concluded that mainly the flexibility of the white goods (i.e. the washing machine, tumble dryer and dishwasher) is used for demand response. The main part of the flexible load of these (smart) appliances is shifted from the evening to the midday, to match local generation. This load shift remained stable over a long period of time (>1 year) and is not responsive to the exact moment of peak-pricing. Therefore, it is concluded that a simple and transparent design for dynamic tariffs is sufficient and most effective to stimulate (manual) residential demand response. Such a tariff should emphasize the ‘right’ moments to use electricity, intuitively linked to renewable generation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".