Browse > Article
http://dx.doi.org/10.5370/KIEEP.2015.64.2.085

Analyzing Smart Grid Energy Data using Hadoop Based Big Data System  

Cho, YoungTak (Department of Information and Computer Engineering, Ajou University)
Lee, WonJin (Department of Computer Science, Kyonggi University)
Lee, Ingyu (Sorrell College of Business, Troy University)
On, Byung-Won (Department of Statistics and Computer Science, Kunsan National University)
Choi, Jung-In (Smart Grid Research Center, Advanced Institutes of Convergence Technology)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers P / v.64, no.2, 2015 , pp. 85-91 More about this Journal
Abstract
With the increasing popularity of Smart Grid infrastructure, it is much easier to collect energy usage data using AMI (Advanced Measuring Instrument) from residential housing, buildings and factories. Several researches have been done to improve an energy efficiency by analyzing the collected energy usage data. However, it is not easy to store and analyze the energy data using a traditional relational database management system since the data size grows exponentially with an increasing popularity of Smart grid infrastructure. In this paper, we are proposing a Hadoop based Big data system to store and analyze energy usage data. Based on our limited experiments, Hadoop based energy data analysis is three times faster than that of a relational database management system based approach with the current system.
Keywords
Smart grid; Big data system; Power data analysis; Hadoop; Energy efficiency;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Smartgrid website, http://www.smartgrid.or.kr, Korea smartgrid association, 2013.
2 Demand Reductions from the Application of Advanced Metering Infrastructure, Pricing Programs, and Customer-Based Systems-Initial Results, SmartGrid.gov, December, 2012.
3 Demand side management using ICT and technologies, Ministry of Trade, Industry and Energy, August, 2013.
4 Big data understanding and applications, B. On, KETEP, December, 2012.
5 Ultra Large-Scale Power System Control Architecture, SmartGrid.gov, October 2012.
6 Hadoop The Definitive Guide, 3rd Ed. W. Tom, OReilly Media, July 2009.
7 Hbase: The Definitive Guide, G. Lars, OReilly, August 2011.
8 Programming Hive, C. Edward, OReilly Media, November 2012.
9 MapReduce: Simplified Data Processing on Large Clusters, J. Dean and S. Ghemawat, Communications of the ACM, Vol. 51, No. 1, pp. 107-113, 2008.   DOI
10 A Big Data Management System for Energy Consumption Prediction Models, W. Lee, B. On, I. Lee and J. Choi, Proceedings of International Conference on Digital Information Management (ICDIM), BangKok, Thailand, pp. 156-161, October, 2014.
11 Simple one-pass algorithm for penalized linear regression with cross-validation on MapReduce, e-Print arXiv:1307.0048 (2013)
12 Development of Sensor Based Energy Management System, D. Um, J. Choi and I. Lee, Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, Vol. 28, No. 10, pp. 69-74, 2014.   DOI
13 A Study on Demand-Side Resource Management Based on Big Data System, J. Yoon, I. Lee and J. Choi, The Transactions of the Korean Institute of Electrical Engineers, Vol. 63, No. 8. pp. 1116:1120, 2014.   DOI   ScienceOn