Data-Driven Predictive Control of Energy Storage in Thermal Inertia

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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.