A fog-based internet of energy architecture for transactive energy management systems
Bibliographic record
Abstract
Internet of Energy (IoE) is a subset of the Internet of Things which covers all aspects of electrical energy systems and provides secure connectivity and interoperability between power grid and Internet. In this paper, we present a fog-based IoE architecture for transactive energy (TE) management systems. The proposed design consists of three different layers. In the first tier, home gateways are employed which collect customers energy consumption data and provide necessary interface between customers and power grid. In the second layer, there are some local fog nodes located at the network edge and provide services with low latency. From the TE system point of view, the fog node act as retail energy market server which provides energy services to the end users. In the third layer, cloud servers are utilized to provide permanent and reliable data storage and high computing power. The proposed architecture supports different communication protocols such as hypertext transfer protocol, constrained application protocol, and OpenADR. We calculate the required bandwidth and delay performance of both fog- and cloud-based models. We present an optimal day ahead energy consumption schedule and an intercustomer energy trading mechanism for exchanging energy between end users. The performance of the proposed architecture is evaluated in terms of different power grid and communication network metrics. Results confirm the superiority of the proposed architecture.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".