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Emerging Technologies for the Energy Systems of the Future

2021· book· en· W4205795211 on OpenAlexafffund
Amjad Anvari‐Moghaddam, Behnam Mohammadi‐Ivatloo, Fazel Mohammadi, Vahid Vahidinasab, Reza Razzaghi

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Windsor
FundersEngineering and Physical Sciences Research CouncilDanish International Development AgencyUdenrigsministerietDepartment of Science and Technology, Ministry of Science and Technology, IndiaNewcastle UniversityFundação para a Ciência e a TecnologiaTrent UniversityHorizon 2020 Framework ProgrammeNiroo Research InstituteIran University of Science and TechnologyNottingham Trent University
KeywordsComputer scienceSystems engineeringEngineering

Abstract

fetched live from OpenAlex

Energy systems are transiting from conventional energy systems to modernized and smart energy systems. This Special Issue covers new advances in the emerging technologies for modern energy systems from both technical and management perspectives. In modern energy systems, an integrated and systematic view of different energy systems, from local energy systems and islands to national and multi-national energy hubs, is important. From the customer perspective, a modern energy system is required to have more intelligent appliances and smart customer services. In addition, customers require the provision of more useful information and control options. Another challenge for the energy systems of the future is the increased penetration of renewable energy sources. Hence, new operation and planning tools are required for hosting renewable energy sources as much as possible.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.142
Threshold uncertainty score0.475

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.005
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1420.085

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 head0.208
Teacher spread0.199 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2021
Admission routes2
Has abstractyes

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