A Controlled Load Estimator-Based Energy Management System for Water Pumping Systems
Bibliographic record
Abstract
This paper presents the development of a controlled load estimator for a water pumping system (WPS) using the data generated from a PSCAD simulation model. A neural network (NN) is trained, using the generated data, to estimate the power demand of the WPS as a function of the control variables. This NN-based load model is then incorporated into the WPS energy management system to determine the optimal operational schedules of the pumps with the objective of minimizing the energy consumption costs and charges associated with peak power demand. Modeling related uncertainties are captured through a novel recursive mechanism for NN retraining, while operational uncertainties are accounted for by applying a receding horizon model predictive control technique. Simulation results indicate notable savings in total energy costs for the WPS facility after applying the proposed strategy as a result of operational schedules optimization and uncertainties mitigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".