Reliability Model for Designing Solar-Powered Center-Pivot Irrigation Systems
Bibliographic record
Abstract
Abstract. Due to the energy-intensive nature of sprinkler irrigation and limited availability of conventional energy resources, viable alternative energy sources are becoming increasingly important. The use of solar photovoltaic (PV) technology in irrigated agriculture is particularly appealing because of the mutual dependence of crop evapotranspiration and potential energy production on incoming solar radiation. However, successful implementation of a PV irrigation system depends on appropriately sizing the PV system components (solar panel array, battery capacity, etc.) under highly variable and site-specific influencing factors, which include the inter-annual variability of the local climate, the crop water requirement, the soil water holding capacity of the field, the irrigation management practice, and the capacity of the irrigation system itself. The objective of this research was to develop a model for assessing the reliability of a PV-powered center-pivot irrigation system. A numerical simulation tool was developed by combining sub-models of the solar power production, battery storage, and irrigation system power requirement, which can be used to evaluate the system performance under variable operating and meteorological conditions. Given the required input variables, the model determines the reliability of the PV system by analyzing the time of irrigation for which the power both produced by the PV array and stored in the batteries is sufficient to fulfill the load demands. The model was validated by comparing the simulated results to field measurements of a small (1.4 ha) solar-powered center-pivot irrigation system installed near Outlook, Saskatchewan, Canada. The utility of the model was demonstrated by simulating the system performance over five years. It was shown that a PV system that has a reliability of greater than 90% is required to avoid moisture stress during dry years for the selected irrigation management strategy. It was further demonstrated how the model may be used to explore different combinations of PV sizing for achieving the desired reliability. This modeling approach is presented as an effective tool to ensure the technical feasibility of a PV irrigation system before the system is built.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".