Energy Data Analytics for Nonintrusive Lighting Asset Monitoring and Energy Disaggregation

Abstract

Business models around energy performance of lighting systems and managed services make increasing use of the energy metering sensor data. However, accurate submetering solutions remain expensive and complex to commission. With the goal of realizing a low-cost metering sensor solution that provides reasonable measurement accuracy, we consider nonintrusive load monitoring for lighting systems using virtual energy submetering. We propose analytics methods using metered lighting energy sensor data to address three problems: 1) provide initial validation of assets of a lighting control system; 2) disaggregate energy consumption across distinct control groups of a validated system enabling usage and performance monitoring; and 3) monitor and manage lighting system asset over lifecycle. The proposed methods are evaluated using an experimental testbed in a commercial building.