Title :
Usage analysis for smart meter management
Author :
Li, Hongfei ; Fang, Dongping ; Mahatma, Shilpa ; Hampapur, Arun
Author_Institution :
IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA
Abstract :
Smart meters gather utility usage data, such as water, electricity and gas readings, by remote reporting. The flood of usage data obtained from each meter in realtime or near real-time enable data analytics and optimization tool to support smart meter management. Predictive usage analytics can provide significant benefits to both the utilities and customers. We propose statistical approaches for meter anomaly detection, usage demand forecasting and association analysis for utility companies. These analyses provide efficient ways to detect malfunctioning meters, optimize water supply in the future and understand the association factors that drive meter failures and water demand. We illustrate our methodology using the automated meter reading (AMR) database from a water utility customer.
Keywords :
demand forecasting; load forecasting; optimisation; power system management; real-time systems; automated meter reading database; data analytics; drive meter failures; malfunctioning meters; meter anomaly detection; near real-time; optimization tool; predictive usage analytics; smart meter management; usage analysis; usage demand forecasting; utility companies; utility usage data; water demand; water utility customer; Analytical models; Demand forecasting; Meteorology; Meter reading; Predictive models; Time series analysis; Water resources; Anomaly Detection; Association Analysis; Automated Meter Reading (AMR); Demand Forecasting; Smart Meter Management; Usage Analysis;
Conference_Titel :
Emerging Technologies for a Smarter World (CEWIT), 2011 8th International Conference & Expo on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4577-1592-1
Electronic_ISBN :
978-1-4577-1590-7
DOI :
10.1109/CEWIT.2011.6135871