A Control Algorithm for Optimal Energy Performance of a Solarium/Greenhouse with Combined Interior and Exterior Motorized Shading
Bibliographic record
Abstract
A solarium/greenhouse attached to a building can provide many benefits, such as collecting solar energy, allowing the cultivation of plants and providing an enjoyable living space to its occupants. Integrating moveable shading devices with fenestration is a recognized way to reduce heat losses and control solar gains. The control strategy for operating shades can have a significant impact on the energy performance of fenestration systems. The aim of this study is to develop an algorithm for the optimum control of combined interior and exterior motorized shading devices based on an energy balance method. The algorithm developed here is designed to maximize solar heat gains while reducing heat losses during the heating season for a cold climate. An attached solarium with motorized shading devices located in Montreal is simulated during the heating season. Results show that heating requirements can be reduced by up to 76% using the proposed algorithm compared to a control scheme based on a fixed solar radiation level. The solarium could collect up to 3.2 MWh (or 134 kWh/m 2 of floor area) of excess heat that could be supplied to the house during the heating season. This algorithm could be useful especially in solariums/greenhouses and could be of interest also for solar houses and high efficiency buildings when increasing solar gains and reducing heat losses is a priority.
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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".