Data association mining for identifying lighting energy waste patterns in educational institutes

A significant portion of the energy consumption in post-secondary educational institutes is for lighting classrooms. The occupancy patterns in post-secondary educational institutes are not stable and predictable, and thus, alternative solutions may be required to match energy consumption and occupancy in order to increase energy efficiency. In this paper, we report an experimental research on quantifying and understanding lighting energy waste patterns in a post-secondary educational institute. Data has been collected over a full academic year in three typical classrooms. Data association mining, a powerful data mining tool, is applied to the data in order to extract association rules and explore lighting waste patterns. The simulations results show that if the waste patterns are avoided, significant savings, as high as 70% of the current energy use, are achievable. (c) 2013 Elsevier B.V. All rights reserved.