A Methodology for the Design of Greenhouses with Semi-Transparent Photovoltaic Cladding and Artificial Lighting
Bibliographic record
Abstract
Greenhouse construction is on the rise in response to a growing demand for fresh local produce and the need for a climate resilient food web. In mid-to-high latitude locations, greenhouses that control light to a consistent daily integral can produce crops year-round by employing heating, horticultural lighting and movable screens. Their energy consumption represents a major production cost and is largely dictated by the envelope design. As an increasing number of envelope materials (including energy generating photovoltaic cladding) become available, methods for determining the most efficient design are needed. \n \nA methodology was developed to assist in identifying the most suitable envelope design from a set of alternatives. First, the energy performance was assessed by conducting integrated thermal-daylighting analysis using building energy simulation software. Then, life cycle cost analysis was employed to determine the most cost-effective design. The methodology was applied to the following three case studies for a mid-latitude (Ottawa (45.4°N), Canada) and a high-latitude location (Whitehorse (60.7°N), Canada): 1) semi-transparent photovoltaic cladding (STPV) applied to the roof; 2) comparison of a glass, polycarbonate and opaque insulation on the walls and roof; and 3) design of ground thermal insulation. \n \nFor Ottawa, the STPV cladding caused internal shading that was counteracted by augmenting supplemental lighting by as much as 84%, which in turn reduced heating energy use by up to 12%. Although STPV cladding increased lighting electricity use, it generated 44% of the electricity that was consumed for supplemental lighting in the present study and 107% in the future projection study. Currently, STPV cladding is not an economically attractive investment unless time-of-use (TOU) electricity pricing is available. However, in the future, a 23% and 37% reduction in life cycle cost (LCC) was achieved for constant and TOU electricity pricing, respectively. STPV will increasingly become a promising cladding alternative for improving energy efficiency and economics of greenhouse operations. By reflecting light onto the crops, an insulated and reflective opaque north wall can lower both lighting electricity and heating energy consumption, while reducing the LCC by 2.6%. The use of ground insulation had a positive albeit negligible impact on energy and economic performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".