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Record W4221138041 · doi:10.1088/1748-9326/ac5cf5

Increasing the reliability of energy system scenarios with integrated modelling: a review

2022· review· en· W4221138041 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Research Letters · 2022
Typereview
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsComputer scienceLeverage (statistics)Nexus (standard)Best practiceEnergy modelingManagement scienceRisk analysis (engineering)Data scienceWork (physics)Efficient energy useEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Systems models are an important tool for policy and energy planning decisions. These models generally fall into one of three modelling paradigms: energy economy, capacity expansion or power sector planning. Recent work seeks to combine these paradigms into an integrated framework to leverage the benefits of different model types. There is also interest and research in representing more system interactions to expand the modelling nexus. However, this increases model complexity and risks creating more black box models that are not well understood or trusted by users or policymakers. To understand the trade-offs and best practices of using combined models, we review current modelling practices, including an overview of the different modelling paradigms in the literature, how combined modelling has been applied to date and how the nexus has been represented in different modelling applications. Building on the literature review, we held a series of expert elicitation workshops to gain insight from energy modelling domain experts who use combined models. Finally, we encapsulate these findings and best practices into a modelling evaluation framework. We find that while there is interest and research being done in these areas, there are no set standards for how to build these types of models, resulting in a wide range of practices. Increasing model complexity to develop fully hard-linked coupled models that are also trustworthy and transparent generally requires more time and resources than is worthwhile. Instead, the focus should be on avoiding black box models by having a clear modelling purpose and developing best practices that allow for clarity and transparency. Expanding the nexus to include attributes such as biodiversity and cultural security presents a challenge and representing them as a cost is not congruent to equitable policy. These aspects could be better incorporated into analysis using stakeholder debate and citizens’ assemblies.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.039
GPT teacher head0.265
Teacher spread0.226 · 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