Abstract
There has been an ongoing effort to increase the number of advanced metering infrastructure (AMI) devices to improve system observability. When deployed across distribution secondary networks, AMI provides building-level load and consumption information, which can be used to improve grid management strategies. A barrier to implementation is the significant upgrade costs associated with retrofitting existing meters with network-capable sensing. One economic way is to use image processing methods to extract usage information from images of the existing meters. This paper presents a solution that uses online data exchange of power consumption information to a cloud server without modifying the existing electromechanical analog meters. In this framework, a systematic approach to extract energy data from images is applied to replace the manual reading process. A case study is presented where the digital imaging approach is compared to the averages determined by visual readings over a one-month period.
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