Research with disaggregated electricity end‐use data in households: review and recommendations
Bibliographic record
Abstract
Changes in electricity systems mean that more detailed information about demand levels for particular energy services in the home are now available to energy researchers. Accordingly, it is useful to determine how these data might be best used by energy researchers. To advance this discussion, 13 studies that use intrusive load‐monitoring techniques to generate, to present, and to make effective use of, disaggregated end‐use electricity data from households are identified. These studies are placed within a broader literature context (including studies using non‐intrusive load‐monitoring techniques), are summarized briefly, and are cross‐compared in order to delineate emerging issues. These issues are as follows: methodological challenges, including monitoring equipment performance and participant recruitment; ways to present the time‐ and space‐specific nature of the end‐use electricity data generated; advances with respect to end‐use electricity models that can be built; appliance‐specific insights; and future priorities for this kind of work, including energy conservation insights, relevant policy recommendations, and priority academic investigations. Finally, reflection upon these 13 studies, as well as the broader energy research agenda, generates a number of priority areas for work going forward: making effective use of additional data; broadening the focus to include electricity production and storage, as well as other energy, carbon, resource, and information flows; placing these data within broader social contexts and wider power system considerations; and encouraging effective use of these data to advance energy system sustainability, at both the household and community levels. WIREs Energy Environ 2015, 4:383–396. doi: 10.1002/wene.151 This article is categorized under: Energy Efficiency > Economics and Policy Energy Infrastructure > Economics and Policy Energy Policy and Planning > Economics and Policy
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".