Historical dimensions and directions on energy storage: unique perspectives
Bibliographic record
Abstract
Energy storage has become a crucial technology solution in an era marked by the urgent need to transition towards sustainable energy systems with renewable sources and energy storage systems. It facilitates the widespread adoption of these sources and ensures the reliability and resilience of energy networks. This perspective article offers a comprehensive overview of the current landscape of energy storage technologies, their diverse applications, and the challenges and opportunities that lie ahead, based on bibliographic data captured from the Scopus. The technological landscape of energy storage methods is examined, encompassing mechanical, heat, chemical, electrochemical, magnetic, and electromagnetic as potential short- and long-duration storage techniques. We discuss trend topics related to the diverse applications of energy storage, ranging from grid integration and electric vehicles to microgrids and ancillary services. Additionally, this study highlights critical research directions and technological advancements that can potentially transform the energy storage sector in the years ahead. This study further aims to provide a valuable contribution to the ongoing discussion on achieving a sustainable, reliable, and decarbonized energy future by comprehending the various aspects and predicting the future trends of energy storage. • The critical role of energy storage in the transition to renewable energy sources. • The diverse range of energy storage technologies and their applications. • The need for efficient and scalable energy storage solutions. • The potential of energy storage to enable a sustainable and decarbonized energy future.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".