SBIR Phase I: Smart Control Automation and Learning for Energy

SBIR 第一阶段：能源智能控制自动化和学习

• 批准号: 2221872
• 负责人: Tanya Barham
• 金额: $ 27.5万
• 依托单位: COMMUNITY ENERGY LABS INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2221872&HistoricalAwards=false
• 关键词: SBIR; Phase; Smart; Control; Automation

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a commercial building management system that uses model predictive control for small to mid-sized commercial building owners to help them flexibly manage increasingly complex energy codes and prices. This technology uses machine learning to automate costly aspects of advanced building control, and eliminating complexity, frustration, and expense for leanly staffed building owners who are attempting to save money, meet code, reduce carbon footprint and adapt to rapidly changing energy prices. The proposed approach significantly reduces the setup time, the amount of training data, and the compute time needed for the technology to converge on accurate models and predictions using building thermal dynamics. These improvements reduce the controller costs without sacrificing accuracy. This technology will simplify the setup and implementation process for under-represented segments in the building automation, efficiency and model-based controls market starting with K-12 schools. This simplification has several distinct societal and environmental benefits including: increased energy and demand charge savings, increased energy efficiency, improved environmental footprint, increased job creation for building controls technicians, improved resiliency, and additional educational opportunities for K-12 families and communities.This SBIR Phase I project develops a technology capable of building efficiency control. The innovation employs a hybrid approach based on constrained deep learning tools that build on physical knowledge of building systems and architecture, thereby making use of sampling data while producing physics-consistent accuracy in modeling and control predictions. Specifically, the project team hopes to converge on an architecture that can more reliably and accurately manage energy use and occupant comfort compared to state of the art control approaches. The project team also aims to demonstrate a significant reduction in heating, ventilation and air-conditioning (HVAC)-driven peak system demand in target buildings while keeping instrumentation, labor, and data costs per building to an affordable cost for the target market.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是开发一个商业建筑管理系统，该系统使用模型预测控制为中小型商业建筑业主提供帮助，帮助他们灵活管理日益复杂的能源法规和价格。这项技术使用机器学习来自动化高级建筑控制的昂贵方面，并为试图节省资金、满足规范、减少碳足迹和适应快速变化的能源价格的精简员工的建筑业主消除复杂性、挫败感和费用。提出的方法显著减少了建立时间、训练数据量和计算时间，从而使该技术收敛于使用建筑热动力学的准确模型和预测。这些改进在不牺牲精度的情况下降低了控制器的成本。这项技术将简化从K-12学校开始的建筑自动化、效率和基于模型的控制市场中未被充分代表的部分的设置和实施过程。这种简化有几个明显的社会和环境好处，包括：增加了能源和需求费用节省，提高了能源效率，改善了环境足迹，增加了建筑控制技术人员的就业机会，提高了弹性，并为K-12家庭和社区提供了更多的教育机会。这项创新采用了一种基于受限深度学习工具的混合方法，该工具建立在建筑系统和建筑的物理知识基础上，从而利用采样数据，同时在建模和控制预测中产生物理一致的准确性。具体地说，项目团队希望汇聚到一种架构上，与最先进的控制方法相比，该架构可以更可靠、更准确地管理能源使用和乘员舒适度。该项目团队还旨在展示目标建筑中由供暖、通风和空调(HVAC)驱动的高峰系统需求的显著降低，同时将每栋建筑的仪器、劳动力和数据成本保持在目标市场可承受的成本。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。