PREDICTOR: Improving the energy performance of closed-loop heating and cooling systems.

预测器：提高闭环加热和冷却系统的能源性能。

• 批准号: 2466348
• 金额: --
• 依托单位: Loughborough University
• 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: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2466348
• 关键词: PREDICTOR; Improving; energy; performance; closed

The research hypothesis is that decision support tools enabling optimum maintenance interventions in closed loop HVAC systems, based on life cycle cost, can deliver significant energy reduction in buildings. The aim of this project is to develop decision support tools to predict when timely maintenance intervention can cost-effectively optimize the operation and maintenance of closed loop HVAC systems in buildings. We aim to test the hypothesis by achieving the following five objectives: 1) Investigate the life cycle performance of different closed loop HVAC systems by analysing commissioning and maintenance data against designed energy performance. 2) Develop data driven HVAC performance models, using machine learning techniques linked to 'live' sensor data, to advise the decision support tool regarding system operational efficiency 3) Develop a decision support tool for HVAC systems (based on a commissioning and operational data, and innovative lifecycle cost benefit analysis), linked to a Building Energy Management framework, enabling real-time intervention to optimise maintenance and energy efficiency. 4) Test and evaluate a prototype of the proposed decision support system for energy management in non-domestic buildings, to enable adoption across the sector and to exploit commercialisation opportunities with industry. 5) Disseminate the results, develop a simple, interactive educational tool and create standards recommendations and guidelines for design, commissioning and maintenance of HVAC systems. PhD timetable: Year 1: Setting up an experimental rig and relating energy consumption to maintenance and design dataYear 2: Developing HVAC performance using data driven models ANN and life cycle costYear 3: Evaluate and test prototype system and develop interactive educational tools, writing up and submitting two journal papers

研究假设是，决策支持工具，使最佳的维护干预闭环HVAC系统，基于生命周期成本，可以提供显着的能源减少建筑物。该项目的目的是开发决策支持工具，以预测何时及时的维护干预可以经济有效地优化建筑物中闭环HVAC系统的操作和维护。我们旨在通过实现以下五个目标来测试假设：1）通过分析调试和维护数据与设计的能源性能，研究不同闭环HVAC系统的生命周期性能。2)开发数据驱动的HVAC性能模型，使用与“实时”传感器数据相关联的机器学习技术，为决策支持工具提供有关系统运行效率的建议3）开发HVAC系统的决策支持工具（基于调试和运行数据，以及创新的生命周期成本效益分析），与建筑能源管理框架相关联，实现实时干预以优化维护和能源效率。4)测试和评估拟议的非住宅建筑能源管理决策支持系统的原型，以便在整个行业中采用，并与行业一起利用商业化机会。5)传播结果，开发一个简单的互动教育工具，并为暖通空调系统的设计、调试和维护创建标准建议和指南。博士课程表：第一年：第二年：使用数据驱动模型ANN和生命周期成本开发HVAC性能第三年：评估和测试原型系统，开发交互式教育工具，撰写并提交两篇期刊论文