Online system optimization algorithm development for building energy management

随着对温室气体排放的担忧加剧以及对未来能源成本的不确定性，提高建筑物能效的需求正在增加。对建筑系统的监控对收集信息很有价值，但尽管进行了大量研究来开发人工智能(AI)支持的控制系统，但商业系统通常仅限于更简单的算法，需要大量时间来部署和定制这些系统以适应特定建筑，并进行季节性调整。因此，使用先进的人工智能方法开发自动控制调整，以允许在线学习和季节性调整是非常可取的。该项目扩展了先前的接洽拨款，证明了该方法在供暖系统中的价值，并将其复制到冷季优化，以最大限度地减少能源消耗，同时保持热舒适条件。我们将在以前在Engage赠款下开发的供暖算法开发方面取得进展的基础上，并在正在进行的实地研究中测试应用程序，以便为未来的改进提供信息，并纳入更积极的天气调整控制预测。采暖季节结束后，我们将收集冷水机组和建筑制冷系统的数据，为多单元住宅的制冷系统开发预测负荷模型和一套控制点优化模型。这些算法将使用基于云的建筑监测和控制系统开发和部署，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，以确认真实建筑中的实际节能效果。

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.