Development of data-driven insights using energy system models: A systematic scoping review
Bibliographic record
Abstract
The prevalence of “What-if” scenario analyses has limited the types of insights that can be produced with energy system models, resulting in insufficient exploration of uncertainty and pathway diversity in the integrated energy system design space. Today, novel data science methods allow modellers to develop valuable insights from complex high-dimensional datasets; this form of analysis is appropriate for the complex questions increasingly posed by stakeholders. However, these methods have yet to be widely adopted, likely due to visibility challenges and perceived high computational cost of producing large results datasets. Identifying and systematizing the extant methods and the resources they require is necessary to promote their adoption. We conducted a systematic scoping review of studies that conduct a data-driven analysis of energy system model outputs. We identified 62 papers that met the inclusion criteria. Of the included manuscripts, there was substantial heterogeneity in modelling framework, analysis approach, and resource requirement, but the breadth of related subdomains indicates a growing role for data scientists in evaluating energy futures. We identified three major application areas: exploration of configurations and trade-offs, distribution of key outcomes under uncertainty, and advancement of modelling methodologies. Finally, we proposed a framework for scoping future data-driven analyses, including the potential role of surrogate models for reducing the computational requirement of high solution volume analyses. Inconsistent reporting practices still weaken the current body of literature; however, standardized reporting and further experimentation will enhance the utility of data-driven analyses, ultimately providing relevant and timely insights to stakeholders.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.113 | 0.369 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.036 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".