Development of an AI-Based regulatory control for an Energy Management Information System (EMIS) to improve occupants health and comfort

开发基于人工智能的能源管理信息系统 (EMIS) 监管控制，以改善居住者的健康和舒适度

• 批准号: 571349-2021
• 负责人: Nasiri, FuzhanF
• 金额: $ 2.19万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746362
• 关键词: Development; AI; Based; regulatory; control

There is a vital need to link data-driven models with Energy Management Information System (EMIS) and Indoor Air Quality (IAQ) control tools to leverage them in real-time settings and evaluate their effects in buildings as a whole. These effects could be occupant related (e.g., such as thermal comfort and health), climate related (e.g., energy usage and emissions), equipment related (e.g., aging of HVAC systems), and cost related (e.g., energy bills, upfront costs, sick leaves, etc.). The EMIS goal is to collect, analyze, optimize, and deliver useful information to the building operators/controls in forms of recommended adjustments, issues, and reports. This is particularly of growing importance in sensitive buildings such as hospitals, schools, and factories with clean rooms (provided the COVID-19 circumstances) to have a clear view of the building operation/controls. The main objective of the proposed project is to establish R&D into the integration of AI technologies, specifically predictive analytics tasks into building HVAC system in order to keep the building healthy, comfortable, and energy efficient with low operating costs. The need for this integration comes from: 1) Availability of massive data related to BMS system and external resources (weather data, and utility bills) recorded in the cloud-based EMIS, 2) The growing need of the industry to have analytics in place to improve efficiency which are currently limited by stringent budgets and resources, and 3) Availability of big data analytics tools, machine learning algorithms, and powerful computational resources to extract patterns from the data for prediction based analytics. The academic and industrial teams will collaborate over the next 2 years to equip this cloud-based EMIS with AI-based analytics tool and validate/test its efficiency-enhancement capability.

将数据驱动模型与能源管理信息系统（EMIS）和室内空气质量（IAQ）控制工具相连接，以在实时设置中利用它们并评估它们对整个建筑物的影响是至关重要的。这些影响可能与居住者有关（例如，例如热舒适和健康），气候相关的（例如，能源使用和排放），设备相关（例如，HVAC系统的老化），以及成本相关的（例如，能源账单、预付费用、病假等）。EMIS的目标是收集、分析、优化并以建议的调整、问题和报告的形式向建筑运营商/控制提供有用的信息。这在敏感建筑物中尤其重要，例如医院、学校和具有洁净室的工厂（假设COVID-19的情况），以清楚地查看建筑物的操作/控制。该项目的主要目标是将人工智能技术，特别是预测分析任务整合到建筑HVAC系统中，以保持建筑物的健康，舒适和节能，同时降低运营成本。这种整合的必要性来自于：1）BMS系统及外部资源相关海量数据的可用性（天气数据和公用事业账单），2）行业对分析的需求日益增长，以提高目前受到严格预算和资源限制的效率，以及3）大数据分析工具，机器学习算法，和强大的计算资源，从数据中提取模式，用于基于预测的分析。学术和工业团队将在未来2年内合作，为这个基于云的EMIS配备基于AI的分析工具，并验证/测试其效率提升能力。