Semantic Digital Twinning for Cost-Optimal HVAC Operation: Real-Time Application to a House with Smart Thermostats and PV/Battery under a Time-of-Use Tariff
Bibliographic record
Abstract
Semantic digital twinning has traditionally supported design coordination, documentation, and planning during the early stages of building projects. However, its application in building operation and maintenance—particularly in real time—remains limited. This study proposes a methodology for cost-optimal HVAC load management using an operational digital twin, and demonstrates its real-time application under a residential time-of-use pricing scheme. The framework is implemented in a grid-connected single-family house equipped with smart thermostats, rooftop photovoltaic panels, and battery storage, and is evaluated under two progressive layers of control and system integration: predictive thermostat control alone, and combined coordination of thermostats, on-site generation, and battery systems. Each configuration is assessed against a static reference derived from two baseline weeks without energy flexibility. Results show that predictive thermostat control reduced electricity costs by an average of 34.7 %, with a total increase in energy import of approximately 84 kWh, while maintaining average indoor temperature deviations below 0.3 °C. Coordinated control achieved 78.4 % average cost savings, reduced net grid import by 115 kWh, and enabled 25.3 kWh of energy export. Relative demand shift analysis confirmed effective load advancement and midday demand reduction, delivering both economic and grid-responsive outcomes. These findings highlight the feasibility of deploying real-time predictive control in operational residential buildings to enhance load flexibility and improve alignment with dynamic electricity pricing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".