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.

建筑物中的温度控制以确保热舒适性，以及冷链中的产品安全储存，占全球二氧化碳排放量的15%。在用于调节温度的加热和冷却设备中，空调和电风扇占全球用电量的10%-其中太多是由于低效的控制而浪费的。这些设备大多采用不灵活的控制架构运行，无法适应人员、产品或电网的时变要求。创建更好的数据驱动算法，优化设备的控制，以减少能源使用，或在使用时进行转换，将被证明是一种具有成本效益的减排策略，并构成该项目的基础。该项目涉及EPSRC的几个研究主题，特别是：能源效率，人工智能技术和能源存储。