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Record W2152974989 · doi:10.1109/tpwrs.2002.1007890

Development of a hybrid model for electrical power spot prices

2002· article· en· W2152974989 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 Power Systems · 2002
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsActuaWestern University
Fundersnot available
KeywordsSpot contractElectricityElectricity marketElectricity priceEconomicsEconometricsStochastic modellingElectricity pricingForward contractElectricity generationElectric power systemElectric powerForward priceComputer scienceMicroeconomicsPower (physics)Financial economicsEngineeringElectrical engineeringFinance

Abstract

fetched live from OpenAlex

A great deal of interest has been paid to the market-based pricing of electrical power. Electrical power contracts often contain embedded options, the valuations of which require a stochastic model for electricity prices. Successful stochastic models exist for modeling price variations in traditional commodities. Electricity is critically different from these commodities as it is difficult to store and, on short time scales, its price is highly inelastic. This has important implications for stochastic spot price models of electricity. Several stochastic models of electricity spot prices already exist. In these random models, price returns play a dominant role. In this paper, we lead a guided tour through existing electricity price data to motivate a new stochastic electricity price model different in that it directly models price. We apply the new model to the problem of pricing options on electrical power and discuss these preliminary results.

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 categoriesMeta-epidemiology (narrow)
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 score0.981
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.210
Teacher spread0.192 · 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