Data-Driven Predictive Control of Energy Storage in Thermal Inertia

热惯性储能的数据驱动预测控制

• 批准号: 2436351
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2436351
• 关键词: Data; Driven; Predictive; Control; Energy

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.

控制建筑物内的温度以实现热舒适，以及控制冷链中的温度以确保产品的安全储存，占全球二氧化碳排放量的 15%。在用于调节温度的供暖和制冷设备中，空调和电风扇的用电量占全球用电量的 10%，其中太多电量因低效控制而被浪费。该设备大多采用不灵活的控制架构来运行，无法适应人、产品或电网随时间变化的要求。创建更好的数据驱动算法来优化该设备的控制以减少能源使用或在使用时进行转换，将证明是一种具有成本效益的减排策略，并构成了该项目的基础。该项目涵盖多个领域EPSRC 研究主题特别是：能源效率、人工智能技术和能源存储。