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Record W2997084050 · doi:10.1002/est2.125

Potential use of thermal energy storage for shifting cooling and heating load to off‐peak load: A case study for residential building in Canada

2019· article· en· W2997084050 on OpenAlexaffabout
Doğan Erdemir, İbrahim Dinçer

Bibliographic record

VenueEnergy Storage · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsThermal energy storageLoad shiftingCooling loadElectricityPeak loadEnvironmental scienceNuclear engineeringEnergy storageThermalPeak demandStorage heaterMaterials scienceMechanical engineeringEngineeringElectrical engineeringMeteorologyPower (physics)Heat pumpThermodynamicsAir conditioningHeat exchanger

Abstract

fetched live from OpenAlex

Abstract This study presents an investigation of the potential use of thermal energy storage for shifting cooling and heating loads to off‐peak hours in order to balance the electricity production and demand periods. In Canada, the main energy source for cooling and heating applications is generally electricity. Therefore, cooling and heating devices have a great impact on the electricity peak load. In this study, it is considered that heating and cooling loads on the electricity peak load periods are shifted to off‐peak hours by thermal energy storage systems. At the end of this study, it is observed that the thermal energy storage has great potential for shifting electricity peak load depending on cooling and heating load to off‐peak periods. The electricity peak loads can be reduced by 25% and 45% by shifting heating and cooling loads to off‐peak hours and doing storage. Furthermore, the thermal energy storage systems can help reduce both cooling and heating costs in Canadian dwellings by 20% and 18%, respectively.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.206
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations35
Published2019
Admission routes2
Has abstractyes

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