A Network of BIMGs Participating in Demand Response Using EVs and HVAC Units
Bibliographic record
Abstract
Distributed energy resources, energy storage systems, photo-voltaic(PV) systems, and electric vehicles charging systems are being increasingly installed in many residential units. This paper presents an optimal power management mechanism for a network of grid-connected buildings integrated with microgrids. The current paper attempts to propose a high-level centralized control problem for apartment-buildings, where each building includes on-roof photovoltaic power unit, stationary local batteries, residential apartments and EVs. We developed a model predictive control for apartment-building energy management system, which controls the heating, ventilation, and air conditioning (HVAC) system, and the electric vehicles. The solution aims to dynamically control the power demand in the building by properly controlling each apartment HVAC in the network in order to improve the matching performance between the renewable power generation and consumption in the network of BIMGs and minimize the power exchange with the main grid. The problem is solved for a network of multi-unit apartments building in Montreal area.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".