The nexus between design and control: a data-driven approach for leveraging flexibility potential of micro-grids
Bibliographic record
Abstract
This research focuses on optimizing energy efficiency and flexibility in institutional and residential buildings within an energy community/micro-grid in Varennes, Québec. Employing data-driven building models, the paper proposes an energy modelling methodology utilising resistance-capacitance (RC) thermal networks to predict the building thermal loads. Electrical base loads are instead evaluated through regression and clustering techniques. The study incorporates on-site solar energy production and communal batteries to comprehensively analyse their impact on the whole energy consumption. The objective is the optimization of thermal and electrical management at both local and communal levels. This is assessed by employing a double-stage model predictive control (MPC) routine, also providing insights on the nexus between design and operation. Emphasizing day-ahead flexibility, the effects of various events on resiliency are assessed. Based on a preliminary energy assessment for the specific period of study, a photovoltaic system ranging from 150 to 200 kWp and a battery storage system ranging from 100 to 200 kWh are recommended for a community made of one house and one institutional building. These systems will ensure efficient energy supply and management, promoting sustainability and reducing reliance on traditional grid sources by over 20%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".