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Record W2012331822 · doi:10.1109/tste.2014.2359795

Solar Power Shaping: An Analytical Approach

2014· article· en· W2012331822 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Sustainable Energy · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDimensioningEnergy storageSizingComputer scienceSolar powerElectric power systemGridKey (lock)Reliability engineeringDistributed generationRenewable energyPower (physics)EngineeringElectrical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

The focus of our work is the use of an energy storage system (ESS) to integrate solar energy generators into the electrical grid. Although, in theory, an ESS allows intermittent solar power to be shaped to meet any desired load profile, in practice, parsimonious ESS dimensioning is challenging due to the stochastic nature of generation and load and the diversity and high cost of storage technologies. Existing methods for ESS sizing are based either on simulation or on analysis, both of which have shortcomings. Simulation methods are computationally expensive and depend on the availability of extensive data traces. Existing analytical methods tend to be conservative, overestimating expensive storage requirements. Our key insight is that solar power fluctuations arise at a few distinct time scales. We separately model fluctuations in each time scale, which allows us to accurately estimate ESS performance and efficiently size an ESS. Numerical examples with real data traces show that our model and analysis are tight.

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.

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: none
Teacher disagreement score0.994
Threshold uncertainty score0.771

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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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