Data-Driven Predictive Control of Energy Storage in Thermal Inertia
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Description
Controlling temperature in buildings for thermal comfort, and in the cold-chain for safe product storage, accounts for 15% of global CO2 emissions. Of the heating and cooling equipment deployed to regulate temperature, air conditioners and electric fans account for 10% of global electricity use - too much of which is wasted through inefficient control. This equipment is mostly operated with inflexible control architecture that does not adapt to the time-varying requirements of people, products or the electricity grid. Creating better, data-driven algorithms that optimise the control of this equipment to reduce energy-use, or shift when it is used, would prove a cost-effective emission mitigation strategy, and forms the basis of this project.
The project cuts across several EPSRC research themes, notably: energy efficiency, artificial intelligence technologies, and energy storage.
| University of Cambridge | LEAD_ORG |
| Emerson | STUDENT_PP_ORG |
| Jonathan Cullen | SUPER_PER |
| Scott Jeen | STUDENT_PER |
Subjects by relevance
- Emissions
- Temperature
- Energy efficiency
- Cooling equipment
- Thermal comfort
- Heating (spaces)
- Decrease (active)
- Climate changes
- Optimisation
- Temperature regulators
- Conditioners
Extracted key phrases
- Inflexible control architecture
- Global electricity use
- Energy Storage
- Thermal Inertia
- Inefficient control
- Predictive Control
- Data
- Global CO2 emission
- Safe product storage
- Effective emission mitigation strategy
- Thermal comfort
- Temperature
- Electricity grid
- Equipment
- Energy efficiency