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

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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.