An Evaluation of Model Predictive Control of Automated Shading to Optimize Passive Solar Gains
Bibliographic record
Abstract
Space heating and cooling account for 65% of the total energy consumption in Canada's residential sector.As a result, the design and construction of passive houses is becoming mainstream.However, passive houses are prone to overheating, even sometimes in the winter.Proper operation of window shading devices can provide substantial savings to the space conditioning loads in homes.Model predictive control (MPC) of automated window blinds has proved to be effective at managing solar gains to reduce the heating and cooling loads in buildings.MPC involves predicting a system's response to a control input over a finite period of time in order to determine the best current control decision.This predictive quality of MPC is beneficial in buildings due to their delayed thermal response to solar gains.This thesis details the framework for an MPC application for automated blind control of a single-family home in Ottawa.Building performance simulation (BPS) software was used to predict the interactions between the blind positions and the energy demand in the home.Optimizations were performed to minimize the total energy demand attributed to heating and cooling the home.The performance of the MPC application was simulated over representative weeks of the year, and compared to a reactive, rule-based controller (RBC), which is the standard practice in blind automation.Energy savings of up to 36% were recorded in comparison to the RBC.iii I would first like to acknowledge my supervisor, Dr. Ian Beausoleil-Morrison for his patience, support, and immense knowledge.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".