Toward integrated crop and building simulation for controlled environment agriculture using EnergyPlus
Bibliographic record
Abstract
• Integrates crop-level energy balance into EnergyPlus using its Python API. • Solves the crop-level energy balance using fixed-point iteration algorithm. • Estimates the hygrothermal loads of controlled environment agriculture spaces. • Validates the model against literature data, demonstrating improved applicability. This paper presents an approach for integrating crop modelling into building performance simulation (BPS) of controlled environment agriculture (CEA) spaces. A comprehensive review of recent literature on CEA energy modelling using building performance simulation (BPS) software highlighted the need for such integrated capabilities. Leveraging EnergyPlus and the Python application programming interface (API), the proposed approach estimates the hygrothermal (sensible and latent) loads within CEA spaces by applying a fixed-point iteration root-finding algorithm based on the crop-level energy balance. The implementation was verified using data from the literature, enhancing the applicability of BPS tools for simulating the unique environmental conditions of CEA spaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".