Parameter-input estimation of RC thermal models of buildings using unscented Kalman filter and nonlinear least square method
Bibliographic record
Abstract
Effective building energy management (e.g. temperature control strategies) necessitates reliable and computationally efficient building thermal models. One type of them is the resistor-capacitor (RC) model. However, estimating model parameters and inputs (e.g. solar heat gain) simultaneously is challenging, especially when some of the temperature states are missing due to instrumentation limitations and/or sensor malfunctions. The present study utilizes unscented Kalman filter (UKF) and nonlinear least squares (NLSs) methods for parameters and input estimation of RC models with possible unavailable temperature states. The estimation procedure, mathematical operations and result analysis are presented in detail. To evaluate the capability of the method, two case studies were conducted. The first case study involved a simple, made-up RC model with known parameters, inputs and states, while the second case study used monitored data from a single detached house. The capability of the method was evaluated by comparing the estimated parameters, inputs and states to the corresponding true values in both study cases. The performance evaluation shows that the proposed method can effectively estimate RC model parameters and inputs, even with certain missing states. The proposed method can be employed for timely online updating of RC model parameters to improve response prediction.
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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 | 0.002 |
| 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".