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Today smart meters are increasingly used in worldwide. Smart meters are the advanced meters capable of measuring customer energy consumption at a fine-grained time interval, e.g., every 15 minutes. The data are very sizable, and might be from different sources, along with the other social-economic metrics such as the geographic information of meters, the information about users and their property, geographic location and others, which make the data management very complex. On the other hand, data-mining and the emerging cloud computing technologies make the collection, management, and analysis of the so-called big data possible. This can improve energy management, e.g., help utilities improve the management of energy and services, and help customers save money. As this regard, the paper focuses on building an innovative software solution to streamline smart meter data analytic, aiming at dealing with the complexity of data processing and data analytics. The system offers an information integration pipeline to ingest smart meter data; scalable data processing and analytic platform for pre-processing and mining big smart meter data sets; and a web-based portal for visualizing data analytics results. The system incorporates hybrid technologies, including big data technologies Spark and Hive, the high performance RDBMS PostgreSQL with the in-database machine learning toolkit, MADlib, which are able to satisfy a variety of requirements in smart meter data analytics.
|Title of host publication||Proceedings of the 10th Conference on Sustainable Development of Energy, Water and Environment Systems|
|Number of pages||14|
|Publisher||International Centre for Sustainable Development of Energy, Water and Environment Systems|
|Publication status||Published - 2015|
|Event||10th Conference on Sustainable Development of Energy, Water and Environment Systems - Dubrovnik, Croatia|
Duration: 27 Sept 2015 → 2 Oct 2015
Conference number: 10
|Conference||10th Conference on Sustainable Development of Energy, Water and Environment Systems|
|Period||27/09/2015 → 02/10/2015|
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