Web services for indoor energy management in a smart grid environment
Bibliographic record
Abstract
Smart grid aims to empower the current power grid with the capability of supporting two-way energy and information flow; and facilitating the integration of advanced computer technology and renewable energy sources into the grid. We assume a smart home with a wireless sensor network based on Zigbee. The smart home contains elements like light or temperature sensors at every room, HVAC (heating, ventilation, and air conditioning), smart appliances, thermostat and smart meter. Furthermore, there is a central computer that can communicate with all these elements. Web service is implemented on central computer and it can be accessed over the internet. The paper proposes an approach that makes use of the web services technologies to remotely interact with smart home elements in a smart grid environment. These interactions include adjusting the temperature according to personal preference or reading energy consumption. Furthermore, utility provider can interact with the smart home via web services and can facilitate demand response or selling energy back to the grid. Some scenarios are shown to describe the interactions in more detail. The performance, advantage and limitations of the radio communications between user (e.g., utility provider) and elements via web services are demonstrated in this paper.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".