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Record W1561924280

Real-Time Feedback and Residential Electricity Consumption: The Newfoundland and Labrador Pilot

2012· preprint· en· W1561924280 on OpenAlexaboutno aff
Dean C. Mountain

Bibliographic record

VenueEconstor (Econstor) · 2012
Typepreprint
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsElectricityConsumption (sociology)Environmental economicsStock (firearms)Sample (material)Energy conservationEconometric modelEnergy consumptionBusinessEngineeringEconomicsEconometrics
DOInot available

Abstract

fetched live from OpenAlex

A pilot study was undertaken in Newfoundland and Labrador to determine whether provision of a real-time feedback device is sufficient to provide residential customers with the information needed to reduce their electricity consumption. A panel based econometric methodology, which controlled for such factors as weather, appliance and housing stock, and demographic determinants influencing electricity consumption, was used to quantify the impacts of the realtime monitor in reducing energy (kWh) use. The study also provided some important insights about socio-economic factors that influence conservation responsiveness, a feature that may assist in developing targeted energy efficiency programs. For example, the electric water heating households showed a higher savings than non-electric water heating households. While positive attitudes toward conservation significantly increase the reduction in electricity when using the real-time monitor, seniors, in their employment of the real-time monitor, do not conserve as much. Overall, the aggregate reduction in electricity consumption (kWh) across the study sample was 18.1%. The paper describes the experimental design, the data collection, the evaluation model, the conservation results, and customers' attitudes and perceptions regarding the real-time monitor.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.238
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

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