Implementation of a Smart Grid Communication System Compliant with IEEE 2030.5
Bibliographic record
Abstract
Energy utilities are constantly under pressure to meet the growing and complicated energy demands. The traditional energy grid allows for one-way communication of energy usage between customers and the utilities. This does not allow the utilities to have control or to suggest any changes in consumption based on the energy data they obtain. In this paper, we propose and implement an innovative two-way communication system between the transformer agent (TA), attached to a neighborhood's electric transformer, and its customer agents (CAs), that are attached to each house using inexpensive and common-use devices and modules. In this context, different houses communicate their energy usage, while an electric transformer relays action requests from the energy utility's headquarters. This enables the real-time tracking of energy usage by both the consumers and the utility. Therefore, the efficiency of energy generation and distribution is enhanced, and consumers are empowered to make smarter decisions about their consumption. In our system, Raspberry Pi3 modules are used to represent CAs, while an Intel Edison is used to represent the TAs. CAs form a self- healing mesh network using the high data rate Wi-Fi in mesh mode while TAs communicate with the utility headquarters using LTE. The proposed system is compliant with the IEEE 2030.5 smart energy profile 2.0 requirements and several tests were performed in real neighborhoods and across the Carleton University campus to prove the system's operation and reliability. This paper is a part of a bigger project to achieve a complete and IoT-compatible platform for future smart grids that includes the whole cycle starting from the Home Energy Management System (HEMS) and ending with data analytics and power consumption prediction in the utility headquarter.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".