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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | King, Chris |
| Copyright Year | 2009 |
| Description | Author affiliation: eMeter Corporation, USA (King, Chris) |
| Abstract | This presentation addresses the challenges associated with integrating advanced metering data into the utility enterprise. These challenges go well beyond simple developing and implementing interfaces that connect the systems that collect advanced meter data to the systems that use the data in business processes, such as billing and outage management. The first step in a useful and successful integration is the basic functions of data collection, data storage, and data delivery in a predictable, timely, and reliable manner. To support these functions, the integration platform must address the inevitable weaknesses associated with any data collection technology as well as the day to day issues that arise in processing and managing such data. For instance, the integration must include a robust data synchronization engine to ensure that the integration software knows the basics - expected data types and sources by meter and service delivery point - as well as more advanced information, such as schedules for delivering billing determinants to the CIS system. As another example, the integration platform must include the tools to detect and resolve data exceptions that arise in the data stream or disrupt the flow of data. Finally, the integration platform must account for and accommodate the limitations of the enterprise systems receiving the data. The outage management system may not be able to handle large data volumes triggered by wide-scale outages, so the integration platform must be able to pre-process, or filter, the outage data to deliver only the most important data. These and related issues will be covered in the presentation. |
| Starting Page | 1 |
| Ending Page | 1 |
| File Size | 254937 |
| Page Count | 1 |
| File Format | |
| ISBN | 9781424438105 |
| DOI | 10.1109/PSCE.2009.4840244 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-03-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Job shop scheduling Load forecasting Clustering algorithms Artificial neural networks Power system planning Predictive models Power system security Power system modeling Power generation Testing |
| Content Type | Text |
| Resource Type | Article |
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