Parameter estimation of electric water heater models using extended Kalman filter
Bibliographic record
Abstract
Electric water heaters have been regarded as a load to be exploited in residential energy management applications due to their potential energy storage capacity. Nevertheless, the implementation of control strategies for water heater systems requires high-performance models which must be capable of reproducing a water heater's internal operation dynamics, especially their inner water temperature variations. Therefore, appropriate water heater model selection and design is a challenge. This paper presents physical parameter estimation methods for two typical water heater models based on experimental data. The first method is based on the evaluation of the on-off times of the heating elements when there is no water consumption; the second method uses an extended Kalman filter to estimate the model's states and physical parameters. Additionally, by leveraging these estimated parameters, a comparative study of the temperature estimation and electric power consumption in both water heater models has been produced and is included. Experimental results show that a precise estimation of the physical parameters in the model allows the water thermal process to accurately identify and predict future power and energy consumption values.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".