A data mining-based framework to define optimal operation strategies for in-duct ultraviolet germicidal irradiation systems for different climatic conditions

This paper presents a novel data mining framework for optimizing the operation of in-duct Ultraviolet germicidal irradiation (UVGI) systems employed for air disinfection within air handling units. The performance of the UVGI system is influenced by the operating conditions of air handling units. As a result, under favourable operating conditions, the system produces excessive Ultraviolet C energy beyond the required disinfection levels, causing energy waste and accelerating the degradation of heating, ventilation, and air conditioning (HVAC) components. The framework aims to improve energy efficiency and mitigate material degradation within HVAC components by systematically adjusting UVGI system output based on changing operating conditions of air handling units throughout the year using advanced data mining techniques. The methodology involves several steps: Firstly, data pre-processing is performed to improve data quality. Then, the K-means clustering algorithm is applied to discover patterns in operational data. Subsequently, an effective performance index and baseline are defined to identify energy-saving opportunities within each cluster. Finally, the optimal operation strategy is defined based on the determined baseline. The framework is extended to five distinct climate zones to derive region-specific operation strategies. Results from a case study in a small office building in Montreal and an extension to five distinct climate zones in Canada demonstrate energy savings from 8% to 37%. This study offers a promising approach to optimize UVGI system operation, with potential applications in various energy systems in buildings requiring adaptive operational conditions for optimal performance.