Maximizing Real-Time Distribution of Wind-Electricity to Electrical Thermal Storage Units for Residential Space Heating
Bibliographic record
Abstract
Wind-electricity is unpredictable in both intensity and duration. This thesis presents the design and implementation of Client-pull and Server-push architectures for the distribution of wind-electricity to Electrical Thermal Storage (ETS) units to match the electrical load of the ETS units with the electricity generation levels. Wind as an energy source is reviewed and the smart grid concept of a communication layer for the transmission, production and usage of electricity is explored. ETS operation is explained and a survey of the Client-pull and Server-push concepts. These implementations are evaluated on their ability to dispatch wind-electricity over a full heating season, short term latency, single day performance and complexity. Client-pull and Server-push architectures have nearly identical performance over a full heating season and identical performance over the 24 hour period evaluated. The Server-push architecture has lower short-term latency but is more complex than the Client-pull.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".