The power of data: Data analytics for M2M and smart grid

Machine to machine (M2M) communication has been gaining momentum in recent years as a key enabling technology for a wide range of applications including smart grid, e-health, home/industrial automation, and smart cities. However, with the current communication systems mainly optimized for human to human communications, there are important capabilities that need to be developed in M2M systems in order to fully realize the new smart services enabled by M2M. In this paper, we provide an overview of M2M and its applications to smart grid. In particular, we discuss technical areas where data mining and machine learning can play an important role in realizing various smart functionalities in the future power grid. As a case study, we also present a novel phase identification technique in smart grid based on smart meter data. Preliminary results have demonstrated the effectiveness of the proposed algorithm.

[1]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[2]  A. W. Kelley,et al.  State estimation for real-time monitoring of distribution systems , 1994 .

[3]  P. A. Brown.  Telecommunications services and local access provision , 1996 .

[4]  Andrew W. Moore,et al.  Internet traffic classification using bayesian analysis techniques , 2005, SIGMETRICS '05.

[5]  S. Perera,et al.  The effect of data aggregation interval on voltage results , 2007, 2007 Australasian Universities Power Engineering Conference.

[6]  Peng Ning,et al.  Mitigating DoS attacks against broadcast authentication in wireless sensor networks , 2008, TOSN.

[7]  Ian H. Witten,et al.  Clustering Documents with Active Learning Using Wikipedia , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[8]  George Michailidis,et al.  Local Vote Decision Fusion for Target Detection in Wireless Sensor Networks , 2008, IEEE Transactions on Signal Processing.

[9]  A. B. Haney,et al.  Smart Metering and Electricity Demand: Technology, Economics and International Experience , 2009 .

[10]  Jianzhong Wu,et al.  Smart metering and self-healing of distribution networks , 2010, 2010 IEEE International Conference on Sustainable Energy Technologies (ICSET).

[11]  Jianhui Wang,et al.  Smart Transmission Grid: Vision and Framework , 2010, IEEE Transactions on Smart Grid.

[12]  Alec Brooks,et al.  Demand Dispatch , 2010, IEEE Power and Energy Magazine.

[13]  T Joseph Lui,et al.  Get Smart , 2010, IEEE Power and Energy Magazine.

[14]  Marco Levorato,et al.  Residential Demand Response Using Reinforcement Learning , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[15]  Vijay Arya,et al.  Phase identification in smart grids , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[16]  Pramode K. Verma,et al.  Maximize user rewards in distributed generation environments using reinforcement learning , 2011, IEEE 2011 EnergyTech.

[17]  Prashant J. Shenoy,et al.  Predicting solar generation from weather forecasts using machine learning , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[18]  Young-Jin Kim,et al.  Cloud-based demand response for smart grid: Architecture and distributed algorithms , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[19]  Georgios Kalogridis,et al.  Data Mining and Privacy of Personal Behaviour Types in Smart Grid , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[20]  Georgios Kalogridis,et al.  ElecPrivacy: Evaluating the Privacy Protection of Electricity Management Algorithms , 2011, IEEE Transactions on Smart Grid.

[21]  Geng Wu,et al.  M2M: From mobile to embedded internet , 2011, IEEE Communications Magazine.

[22]  Yogesh L. Simmhan,et al.  An Informatics Approach to Demand Response Optimization in Smart Grids , 2011 .

[23]  Kim Chang,et al.  Global Wireless Machine-to-Machine Standardization , 2011, IEEE Internet Computing.

[24]  Carlos Ramos,et al.  AI in Power Systems and Energy Markets , 2011, IEEE Intell. Syst..

[25]  Nei Kato,et al.  An early warning system against malicious activities for smart grid communications , 2011, IEEE Network.