Data-Driven Predictive Control of Energy Storage in Thermal Inertia
Lead Research Organisation:
University of Cambridge
Department Name: Engineering
Abstract
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.
The project cuts across several EPSRC research themes, notably: energy efficiency, artificial intelligence technologies, and energy storage.
People |
ORCID iD |
Jonathan Cullen (Primary Supervisor) | |
Scott Jeen (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/R513180/1 | 01/10/2018 | 30/09/2023 | |||
2436351 | Studentship | EP/R513180/1 | 01/10/2020 | 30/09/2024 | Scott Jeen |
EP/T517847/1 | 01/10/2020 | 30/09/2025 | |||
2436351 | Studentship | EP/T517847/1 | 01/10/2020 | 30/09/2024 | Scott Jeen |