Application of Predictive Control Strategies in a Net Zero Energy Solar House
Bibliographic record
Abstract
ABSTRACT: The current availability of online weather forecasts, with increasingly abundant and detailed information, facilitates the implementation of predictive control strategies in buildings, a measure that can help in reducing energy consumption and peak loads, and in improving comfort. These forecasts are even more useful in the case of solar-optimized buildings, where the estimation of future conditions (especially solar radiation availability) is essential for planning a sequence of control actions. This paper presents methods to incorporated weather forecasts into the control system of a solar house, focusing on applications for a cold climate. Simulation results employing Simulink (a MATLAB® based tool) are presented for the particular case of a solar house under Montréal weather conditions. It has been found that predictive control, by helping to manage stored thermal energy, becomes essential to enhance the performance of a building integrated photovoltaic thermal (BIPV/T) system. The use of predictive control permits cutting down the utilization of the backup heat source, and the reducing the total electric energy consumption of the heat pump by 23.4%. Simulations indicate that a BIPV/T roof can supply 70 % of the auxiliary heating needed by a house in Montréal during the month of February.
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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 | 0.000 | 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) | 0.003 | 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".