Optimization of Thermal Dynamic Models for MPC-Based Sustainable Building Energy Systems
Bibliographic record
Abstract
This paper investigates the practical implementation of models in the energy management of buildings for complex user behavior and the use of multiple heating technologies, focusing on the development of an accurate yet efficient model.The study is exemplified by the new Institute for Hydrogen and Energy Technology building at Hof University of Applied Sciences, designed as a research platform for innovative energy solutions.We address the integration of shading strategies and the subsequent model order reduction necessary for effective Model Predictive Control application.The research involves creating a simplified resistance-capacitance model of the building's thermal zones, including its heating systems and a dual façade with solar thermal collectors.This simplified model, generated using the BRCM Toolbox and validated against a detailed EnergyPlus model, accounts for dynamic discrepancies, particularly during periods of high solar radiation.Optimization techniques are applied to the simplified model across different seasons, revealing that season-specific optimizations are more effective for long-term simulations, while a combined optimization approach is suitable for short-term and yearround MPC applications.The results underscore the potential of advanced MPC strategies to enhance energy efficiency and sustainability in complex building systems with multiple renewable energy sources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".