Heating and cooling supply estimation to control buildings temperature using resistor-capacitor thermal model, unscented kalman filter, and nonlinear least square method
Bibliographic record
Abstract
Buildings can have varying heating and cooling set points to take advantage of favorable environmental conditions and low time-of-use rates. To optimize temperature scheduling and energy planning, building energy managements need reliable building thermal models and efficient estimation methods to accurately estimate space heating and cooling supply (or power demand) over a certain period (e.g., 24 h). This accurate estimation capability is vital for performing temperature control strategies. Therefore, the present study used resistor-capacitor (RC) models and unscented Kalman filter (UKF) integrated with nonlinear least square (NLS) to develop a method for precisely estimating heating and cooling supply to control zone temperature. To evaluate the capability of the method, two case studies are conducted. The first case study involves a made-up simple RC model, while the second case study uses monitored data from a single detached house in different scenarios. The capability of the method is evaluated by applying the estimated heating and cooling supply to the RC thermal model and simulated zone temperatures. Then, assess whether the controlled zone's temperature meets the expected temperature or not. The performance evaluation shows that the developed method can accurately estimate the heating and cooling supply, validating its applicability to temperature control objectives. Practical Application: This research provides a valuable contribution to modern building industry professionals by offering a precise method for estimating heating and cooling supply for temperature control in buildings. By equipping practitioners with an effective tool to optimize energy management, this study addresses a critical aspect of building performance. The practical case studies demonstrate the versatility and applicability of this approach in real-world scenarios. In a world increasingly prioritizing energy efficiency and sustainability, this research empowers professionals to make informed decisions, enhance building performance, and contribute to a greener and more sustainable future, all within a concise and actionable framework.
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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 | 0.000 |
| 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".