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Record W1526600169 · doi:10.1109/ihtc.2014.7147535

A Consumer Bill of Rights for Energy Conservation

2014· article· en· W1526600169 on OpenAlexafffund
Stephen Makonin, Laura Guzman Flores, Robyn Gill, Roger Alex Clapp, Lyn Bartram, Bob Gill

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsBritish Columbia Institute of TechnologyUniversity of British ColumbiaGovernment of British ColumbiaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaU.S. Consumer Product Safety Commission
KeywordsEnergy conservationEnvironmental economicsWork (physics)PurchasingBusinessEnergy consumptionGovernment (linguistics)Consumption (sociology)Power (physics)Purchasing powerEconomicsMarketingEngineering

Abstract

fetched live from OpenAlex

Sustainable energy supply and demand can partially be solved by the conservation of energy, which is a personal and self-driven action. However, energy conservation currently requires the purchase of third-party products. The upfront cost of purchasing these products to monitor energy consumption in a home is a barrier that further cements the divide of those that have and those that have not. Detailed appliance power consumption reporting should be made available for free as part of the home's smart meter. Governments and power utilities must improve and expand policies that promote a socio-economic balance allowing everyone to participate in energy conservation regardless of their economic situation in a sustained way. We critically look at what economics and government polices exist and need to exist. We also demonstrate the computational means to achieve this - nonintrusive load monitoring (NILM) - and discuss how manufacturing and standards organizations need to work together to provide the essential information that describes how appliances consume energy. This paper proposes a Consumer Bill of Rights for Energy Conservation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.165

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.177
Teacher spread0.170 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations13
Published2014
Admission routes2
Has abstractyes

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