Control of the ICE-Harvest System using an Advanced Multi-Agent Framework
Bibliographic record
Abstract
This thesis presents the development of an advanced control structure for the novel Integrated Community Energy and Harvesting (ICE-Harvest) system. The ICE-Harvest system is similar to a District Energy (DE) system in many ways and is different in others. It consists of a set of energy producing and storage units coupled to a thermal network which connects residential and commercial buildings of various types. Its operation is geared towards addressing challenges associated with the current energy landscape in Ontario, with potential to be applicable elsewhere in Canada. The control of this system is based on the implementation of a Multi-Agent System (MAS) combined with Sequential Logic Controllers (SLCs). Results demonstrate the capability of the developed control framework to operate the ICE-Harvest system while ensuring the thermal and electrical energy supply to the different consumers. Based on the results presented, the operation of the ICE-Harvest system by the developed control framework is compared with that of a Business-As-Usual (BAU) scenario. This comparison demonstrates the capability of the developed control framework to operate the ICE-Harvest system in a way that also increases the energy utilization and reduces emissions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".