MPC-Based Efficient Energy Control and Cost Estimation of HVAC in Buildings
Bibliographic record
Abstract
The resistor-capacitor network (RC models) is a common approach to model thermal systems in buildings that proves advantageous in improving a building's energy efficiency. This paper presents a framework for energy consumption cost estimation and power efficient control in buildings-based model predictive control. The proposed framework calculates the electricity cost for dwellings based on their sizes using RC models. Additionally, it ensures consistent thermal comfort within the controlled building even during performance issues. The calculation of the cost of energy consumption takes into account the electricity tariff provided by Hydro-Quebec, Montreal, Quebec, Canada. Model Predictive Control (MPC) along with two backup controllers (ON/OFF control and Proportional-Derivative-Integral (PID) control) optimize the thermal model in a building, ensuring the desired indoor temperature efficiently with low cost. The Simulation results conducted on the Matlab/Simulink platform demonstrated that MPC control outperforms the other controllers in terms of energy consumption minimization and cost.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".