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Regional differentiation of energy consumption in urban households

2021· article· en· W3158005764 on OpenAlexaboutno aff
V.G. Mokhov, Maria Nikolaevna Eltsova, Martin Bauer

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicEnergy Load and Power Forecasting
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy consumptionConsumption (sociology)Energy (signal processing)Energy demandQuarter (Canadian coin)EconomicsBusinessEnvironmental economicsNatural resource economicsGeographyEngineering

Abstract

fetched live from OpenAlex

Abstract The paper considers a very important problem for modern world - the energy consumption and energy saving. Nowadays households consume more than a quarter of global energy consumption (29%), which means that this sector has great savings potential. However, this potential for savings can only benefit upon development of proper structural policy measures and laws and implementation of appropriate energy-saving measures. This requires information on the future trends of energy demand and on the factors influencing this demand and the actual energy consumption. The main challenge for researchers presents the fact that energy consumption in the regional aspect is extremely uneven, structurally heterogeneous, which assumes the different measures for the energy saving in each region of the country. Besides, we were unable to find publications describe the contribution of various factors to the dynamics of the energy consumption of the regional economy at the municipal level. Hence, the paper analyzes stream-lined trends in the energy consumption in households of Germany which is one of the world leaders in these issues. So the paper highlights some factors as well as some trends affecting the energy consumption in households in the Perm region.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.330

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 head0.029
GPT teacher head0.217
Teacher spread0.188 · 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 designBench or experimental
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

Citations0
Published2021
Admission routes1
Has abstractyes

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