Optimal Load Profile Management in Extensive Agricultural Regions Using Water Storage Systems
Bibliographic record
Abstract
In this paper, a new algorithm is proposed to smooth the load profile of a wide area agricultural region via utilization of both demand side management and storage system integration. The proposed algorithm aims to schedule the operation of pumps in each agricultural region throughout the day, which results in a smoothed load profile. Unlike traditional agricultural practices where pumps are typically operated simultaneously and thus increasing the system’s peak demand, the pumps in each region are scheduled to operate at different times, and the water received from the well is stored in a water tank. The stored water is then used to meet the crop needs at any time, eliminating the need to synchronize pump operations and irrigation processes. A Binary Mixed-Integer Programming optimization problem is developed, where the decision variables are represented as a matrix that specifies the schedule of pump operations. The objective function of the optimization problem is defined as the Root Mean Square of the difference between the load profile and the average load. The optimization problem is subject to a constraint that ensures the required amount of water for crops are always met during the irrigation process, either through the use of stored water in the water tank or by running during the irrigation time.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".