Online system optimization algorithm development for building energy management

建筑能源管理在线系统优化算法开发

• 批准号: 538475-2019
• 负责人: Mcarthur, Jennifer
• 金额: $ 0.91万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Engage Plus Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=675167
• 关键词: Online; system; optimization; algorithm; development

The need for improved energy efficiency in buildings is increasing as concerns over GHG emissions rise and uncertainty over future energy costs. Monitoring of building systems is valuable to gather information but despite substantial research to develop artificial intelligence (AI)-supported controls systems, commercial systems are typically limited tosimpler algorithms and require significant time to deploy and tailor these systems to a particular building, alongwith seasonal adjustment. Therefore, the development of automated control tuning using advanced AIapproaches to permit online learning and seasonal adjustment is extremely desirable.This project extends a previous ENGAGE grant that demonstrated the value of this approach for heating systems and replicate it for cooling season optimization, to minimize energy use while maintaining thermal comfort conditions. We will build on previous advances in heating algorithm development developed under the Engage grant and test the applications in ongoing field studies to inform future refinements and incorporate more aggressive weather-adjusted control predictions. Once the heating season is over, we will collect chiller and building cooling system data to develop both a predictive load model and set of control point optimization models for the cooling systems for Multi-Unit Residential Buildings. These algorithms will be developed and deployed using a cloud-based building monitoring and control system, with updated algorithms provided to the industry partner on a monthly basis starting in Month 4 of the grant to permit in-situ testing and ongoing refinement during the cooling system. By working with existing facilities, the algorithms will be tested and deployed on pilot projects to confirm actual energy savings in real buildings.

随着对温室气体排放的担忧增加和未来能源成本的不确定性，提高建筑物能源效率的需求正在增加。建筑系统的监控对于收集信息很有价值，但尽管对开发人工智能（AI）支持的控制系统进行了大量研究，但商业系统通常仅限于简单的算法，并且需要大量时间来部署和定制这些系统以适应特定的建筑，以及季节性调整。因此，使用先进的人工智能方法来实现在线学习和季节调整的自动控制调整的发展是非常可取的。该项目扩展了先前的ENGAGE资助，该资助证明了这种方法在供暖系统中的价值，并将其复制用于冷却季节优化，以最大限度地减少能源使用，同时保持热舒适条件。我们将在Engage赠款下开发的加热算法开发方面取得进展，并在正在进行的实地研究中测试应用程序，以告知未来的改进，并纳入更积极的天气调整控制预测。一旦供暖季节结束，我们将收集冷水机组和建筑冷却系统数据，为多单元住宅建筑的冷却系统开发预测负荷模型和控制点优化模型。这些算法将使用基于云的建筑监测和控制系统进行开发和部署，并从赠款的第4个月开始每月向行业合作伙伴提供更新的算法，以便在冷却系统期间进行现场测试和持续改进。通过与现有设施合作，这些算法将在试点项目中进行测试和部署，以确认真实的建筑物中的实际节能效果。