Estimating the impact of crops on peak loads of a Building-Integrated Agriculture space
Bibliographic record
Abstract
In a building-integrated agriculture (BIA) space, peak loads must be estimated to size HVAC equipment in order to maintain indoor air conditions that enhance crop growth. However, the estimation of the rates of heat gain/loss induced by the crops and their impact on heating and cooling loads have only been sparsely addressed. The present study proposes a workflow to estimate the impact of crops on a BIA space peak loads. The building, BIA space and crops – lettuces – are modeled in TRNSYS 18, while loads for design day conditions are assessed by completing a parametric study that varied the cultivated density (CD), the indoor air conditions (temperature and humidity) and the leaf area index (LAI) of the crops. Compared to the baseline peak loads, the estimated sensible heating and latent cooling peak loads of the BIA space at the highest CD for a LAI of 2.1 are 3.6 to 3.7 and 1.1 to 2.1 times higher, while being 13.3 to 14.0 and 6.0 to 9.9 times higher for a LAI of 10. The results show the importance of considering crops in estimating peak loads to size HVAC equipment and promote crop yield.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".