Abstract: Many public utilities companies employ meter data management systems (MDMSs) to manage the millions and sometimes billions of daily and interval meter read values that they collect during a single billing period. One problem with some MDMSs estimate missing daily reads based on average daily usage and these estimates do not match daily usage values determined from interval read data, causing sum check exceptions. Such exceptions are undesirable because they can lead to billing errors and lost consumer confidence in the utility, among other things. To address this, the present inventors devised an exemplary system that uses good and estimated interval read data to more accurately estimate the missing daily reads and thereby reduce occurrences of sum check exceptions. The exemplary system also estimates missing time of use and demand data based on interval read data.

Abstract: The present inventors recognized a need to leverage the data from Advanced Metering Systems (AMIs) in the management of electrical outages. To address this and/or other needs, the present inventors devised, among other things, systems, methods, and software that not only intelligently filter and communicate AMI outage data to Outage Management Systems (OMSs), but also facilitate communications between OMSs and multiple types of AMI systems. One exemplary system includes an outage management module that receives AMI outage data in the form of “last gasp” messages from meters and determines whether the OMS is already aware of a power outage associated with those meters. If the OMS is already aware of an outage associated with these meters, the outage management module excludes the outage reports from its communications with the OMS, thereby preserving its capacity to handle new outage information and overcoming a significant obstacle to using AMI data within OMSs.

Abstract: A method for processing daily consumption, demand and time of use meter reads for electric, gas, water and other metered entities. The meter reads are collected by any of the well known Automated Meter Reading (AMR) technologies and loaded into a meter data warehouse. At a minimum daily meter reading, reads that are tagged as Good, Stale, Partial, Incomplete and Missing are required for properly identifying usage patterns, applying rules for error patterns and estimating the reads which will provide quality meter reads to the utilities thereby identifying meter problems before they become billing problems. The overall process involves loading the daily meter reads into a database, comparing each meter read against its previous day's read and applying a set of rules that help in validating, editing and estimating (VEE) of this data. The VEE rules that are applied can be broadly classified into five (5) categories.