Commercial greenhouse water demand sensitivity analysis: single crop case study
Bibliographic record
Abstract
Today water distribution utilities are trying to improve operational efficiency through increased demand intelligence from their largest customers. Moving to prognostic operations allows utilities to optimally schedule and scale resources to meet demand more reliably and economically. Commercial greenhouses are large water consumers. In order to produce effective forecasting models for greenhouse water demand, the factors that drive demand must be enumerated and prioritized. In this study greenhouse water demand was modeled using artificial neural networks trained with a dataset containing eight input factors for a commercial greenhouse growing bell peppers. The dataset contained water usage, climatic and temporal data for the years 2012–2014. This model was then evaluated using the Extended Fourier Amplitude Sensitivity Test, a global sensitivity analysis, in order to determine the importance, or sensitivity, of each input factor. It was found that time of day, solar radiation, and outdoor temperature (°C) had the largest effects on the model output. These outputs could be used to contribute to the generation of a simplified demand-forecasting model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".