Physics-informed Machine Learning Modelling for Multi-scale Building Energy Systems with Enhanced Accuracy and Interpretability
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Physics-informed Machine Learning Modelling for Multi-scale Building Energy Systems with Enhanced Accuracy and Interpretability
CoPED ID
fc3174df-89f2-412f-9e07-00073d80ad51
Status
Active
Funder
Value
No funds listed.
Start Date
Sept. 25, 2022
End Date
Sept. 24, 2026
Description
Buildings significantly contribute to global energy consumption and carbon emissions and play an important role in accelerating the transformation of a low-carbon energy system. Accurate and transparent modelling is essential for developing energy-flexible and resilient buildings, characterising building demand flexibility, and comprehensively assessing different control strategies before implementation. In practice, physics-based building modelling requires many inputs, some uncertain; this often leads to overly simplistic or inaccurate approaches, especially in large-scale multi-building contexts. Pure data-driven modelling approaches are attractive but lack interpretability, and model predictions are not fully explainable or trustable.
| University College London | LEAD_ORG |
Subjects by relevance
- Emissions
- Energy consumption (energy technology)
- Modelling (creation related to information)
- Energy economy
- Construction
- Energy efficiency
- Forecasts
Extracted key phrases
- Scale Building Energy Systems
- Building modelling
- Building demand flexibility
- Scale multi
- Building context
- Resilient building
- Carbon energy system
- Machine Learning Modelling
- Global energy consumption
- Physics
- Modelling approach
- Transparent modelling
- Enhanced Accuracy
- Carbon emission