Optimization of Energy Systems using MILP and RC Modeling: A Real Case Study in Canada
Bibliographic record
Abstract
In today’s world, where technology is rapidly evolving and people are becoming more concerned about cost and comfort, the demand for electricity for heating, ventilation, and air conditioning (HVAC) systems is on the rise. This puts a strain on efficient power grid management. Demand response strategies have become a critical solution to address this issue. One method is to utilize the inherent inertia of HVAC systems to regulate energy consumption intelligently. This paper presents an optimization model specifically designed for the Varennes Library in Quebec, which is a Net-Zero building. The purpose of the model is to evaluate the energy adaptability of the building and explore the interplay between various systems, such as a building-integrated photovoltaic array, a geothermal heat pump, and a thermal storage-equipped radiant floor system. The approach used to develop the model is based on a mixed-integer linear programming problem, which incorporates a resistance-capacitance (RC) model that describes the building’s thermal behavior. The model is validated through on-site measurements, which enable a rigorous quantitative analysis of the building’s potential flexibility, particularly under different weather conditions. The study assesses proposed control strategies in contrast to baseline measures, providing valuable insights into efficient energy management within the context of the Varennes Library.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".