Optimal Real-Time Energy Management in Apartment Building Integrating Microgrid With Multizone HVAC Control
Bibliographic record
Abstract
Today, distribution systems are presently transforming from a demand-driven to an active asset-driven activity, portrayed by expanding measures of decentralized generation units and an increasing participation of end users in demand response programs. The role of residential buildings will change to an active player in the power grid, either by integrating distributed energy resources onsite and even by an active orchestration of local demand. This article presents an effective approach for the modeling and optimization of a multiunit residential or multiple dwelling units building, integrating a local shared renewable power generation, energy storage system, and electric vehicles. We aim to support the decision-making in the context of energy consumption for a multiunit building through developing a model predictive control able to effectively control the heating, ventilation, and air conditioning system in each apartment of the building in order to reduce the electric bill of the building and improve the matching performance between the local generation and consumption. The problem is solved for a multiunit apartments building in the Montreal area. The results show the efficiency of proposed method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".