MétaCan
Menu
Back to cohort
Record W2127438382 · doi:10.1109/tpwrs.2009.2030387

Climate Change Impacts on Residential and Commercial Loads in the Western U.S. Grid

2009· article· en· W2127438382 on OpenAlexaboutno aff
Ning Lü, Todd Taylor, Wei Jiang, Chunlian Jin, James Correia, L. Ruby Leung, Pak Chung Wong

Bibliographic record

VenueIEEE Transactions on Power Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
FundersNational Renewable Energy LaboratoryDepartment of Atmospheric Sciences, Texas A and M UniversityTexas A and M University
KeywordsClimate changeEnvironmental sciencePeak loadAir conditioningGridEnergy consumptionMeteorologyEngineeringAutomotive engineeringGeographyElectrical engineeringEcology

Abstract

fetched live from OpenAlex

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents a multidisciplinary modeling approach to quickly quantify climate change impacts on energy consumption, peak load, and load composition of residential and commercial buildings. This research focuses on addressing the impact of temperature changes on the building cooling load in ten major cities across the Western United States and Canada. Our results have shown that by the mid-century, building yearly energy consumption and peak load will increase in the Southwest. Moreover, the peak load months will spread out to not only the summer months but also spring and autumn months. The Pacific Northwest will experience more hot days in the summer months. The penetration levels of air-conditioning (a/c) systems in this region are likely to increase significantly over the years. As a result, some locations in the Pacific Northwest may be shifted from winter peaking to summer peaking. Overall, the Western U.S. grid may see more simultaneous peaks across the North and South in summer months. Increased cooling load will result in a significant increase in the motor load, which consumes more reactive power and requires stronger voltage support from the grid. This study suggests an increasing need for the industry to implement new technology to increase the efficiency of temperature-sensitive loads and apply proper protection and control to prevent possible adverse impacts of a/c motor loads. </para>

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.416

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 head0.016
GPT teacher head0.231
Teacher spread0.216 · 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 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

Citations43
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Power SystemsSame topicBuilding Energy and Comfort OptimizationFrench-language works237,207