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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".