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Record W4237450255 · doi:10.1002/9781119283362.ch3

A Balance‐Space‐Based Operation Strategy for <scp>CCHP</scp> Systems

2017· other· en· W4237450255 on OpenAlexaff
Yang Shi, Mingxi Liu, Fang Fang

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPower (physics)Relation (database)Power BalanceFunction (biology)Energy (signal processing)Mathematical optimizationControl theory (sociology)Optimal designComputer scienceEngineeringMathematicsArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

Performance and efficiencies of Combined Cooling, Heating, and Power (CCHP) systems mainly depend on system structures, operation strategies, and choices of facility capacity. This chapter explores the energy flow of the CCHP system, and the optimal operation strategies for the CCHP system with unlimited and limited power generation unit (PGU) capacities. It focuses on the evaluation criteria (EC) function used in choosing different strategies constrained by primary energy rates. The chapter also presents the mathematical model of the optimization problem and a case study to verify the feasibility of the proposed optimal operation strategies and optimal PGU capacity. To be more practical, an optimal operation strategy, which is based on the relation between full capacity output of the PGU and energy load, is designed for the CCHP system with limited PGU capacity. The enumeration algorithm is adopted to obtain the optimal PGU capacity.

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.239
Teacher spread0.221 · 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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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