Cloudhive: A Cloud‐Based Framework for Smart Grid Co‐Simulation, Data, and Communication
Bibliographic record
Abstract
ABSTRACT The integration of renewable energy has driven the need for smart grid frameworks that enable efficient co‐simulation, data management, and secure communication. This paper introduces CloudHive, a cloud‐native framework designed to address these challenges by unifying large‐scale power‐network co‐simulation, real‐time data communication, and big data analytics in a single modular architecture. Unlike existing co‐simulation tools or data platforms that operate in isolation, CloudHive uniquely enables bidirectional interaction between simulation environments (e.g., OpenDSS for power systems, OMNeT++ for communication networks) and real‐world smart grids, supported by message‐oriented middleware (RabbitMQ, Apache Kafka) for low‐latency data exchange and Kubernetes for dynamic scalability. We evaluate CloudHive's accuracy, scalability, and usability through three representative case studies. The results show that CloudHive achieves high accuracy, performs well in real‐world scenarios, and scales efficiently with growing workloads in cloud environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".