From Smart Meter Data to Pricing Intelligence - Visual Data Mining towards Real-Time BI

The deployment of smart metering in the electricity industry has opened up the opportunity for real-time BI-enabled innovative business applications, such as demand response. Taking a holistic view of BI, this study introduced a visual data mining driven application in order to exemplify the potentials of real-time BI to the electricity businesses. The empirical findings indicate that such an application is capable of extracting actionable insights about customer’s electricity consumption patterns, which will lead to turn timely measured data into pricing intelligence. Based on the findings, we proposed a real-time BI framework, and discussed how it will facilitate the formulation of strategic initiatives for transforming the electricity utility towards sustainable growth. Our research is conducted by following the design science research paradigm. By addressing an emerging issue in the problem domain, it adds empirical knowledge to the BI research landscape.

[1]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[2]  Alan R. Hevner,et al.  The Three Cycle View of Design Science , 2007, Scand. J. Inf. Syst..

[3]  T. Kohonen,et al.  Visual Explorations in Finance with Self-Organizing Maps , 1998 .

[4]  J. Aken Management Research Based on the Paradigm of the Design Sciences: The Quest for Field-Tested and Grounded Technological Rules , 2004 .

[5]  Jay F. Nunamaker,et al.  Systems Development in Information Systems Research , 1990, J. Manag. Inf. Syst..

[6]  Rozan O. Maghrabi,et al.  The Role of Business Intelligence (Bi) in Service Innovation: an Ambidexterity Perspective , 2011, AMCIS.

[7]  Alan R. Hevner,et al.  Integrated decision support systems: A data warehousing perspective , 2007, Decis. Support Syst..

[8]  Barbara Wixom,et al.  Continental Airlines Flies High with Real-Time Business Intelligence , 2004, MIS Q. Executive.

[9]  O Marjanovic,et al.  BI-Enabled, Human-Centric Business Process Improvement in a Large Retail Company , 2011, 2011 44th Hawaii International Conference on System Sciences.

[10]  Graham J. Williams,et al.  Data Mining , 2000, Communications in Computer and Information Science.

[11]  Juhani Iivari,et al.  A Paradigmatic Analysis of Information Systems As a Design Science , 2007, Scand. J. Inf. Syst..

[12]  Jarmo,et al.  IMPROVING SHORT-TERM LOAD FORECAST ACCURACY BY UTILIZING SMART METERING , 2010 .

[13]  Mayuram S. Krishnan,et al.  BI and CRM for Customer Involvement in Product and Service Development , 2011, ICIS.

[14]  Björn Niehaves,et al.  BI Systems Managers' Perception of Critical Contextual Success Factors: A Delphi Study , 2011, ICIS.

[15]  Barbara Wixom,et al.  The Current State of Business Intelligence , 2007, Computer.

[16]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[17]  Barbara Wixom,et al.  Real-Time Business Intelligence: Best Practices at Continental Airlines , 2005, Inf. Syst. Manag..

[18]  Galit Shmueli,et al.  Predictive Analytics in Information Systems Research , 2010, MIS Q..

[19]  Samuel Kaski,et al.  Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997 , 1998 .

[20]  T. E. Marshall,et al.  Business intelligence: an analysis of the literature , 2008 .

[21]  Mohammed H. Albadi,et al.  Demand Response in Electricity Markets: An Overview , 2007, 2007 IEEE Power Engineering Society General Meeting.

[22]  R.E. Abdel-Aal Short-term hourly load forecasting using abductive networks , 2004, IEEE Transactions on Power Systems.

[23]  T. Kohonen,et al.  Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum , 2003 .

[24]  Randall S. Collica CRM Segmentation and Clustering Using SAS Enterprise Miner , 2007 .

[25]  S. Chatterjee,et al.  Design Science Research in Information Systems , 2010 .

[26]  Barbara Wixom,et al.  The BI-Based Organization , 2010, Int. J. Bus. Intell. Res..

[27]  Antti MUTANEN CUSTOMER CLASSIFICATION AND LOAD PROFILING BASED ON AMR MEASUREMENTS , 2011 .

[28]  Salvatore T. March,et al.  Design and natural science research on information technology , 1995, Decis. Support Syst..

[29]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[30]  Veda C. Storey,et al.  Design science in the information systems discipline: an introduction to the special issue on design science research , 2008 .

[31]  Omar El Sawy,et al.  Building an Information System Design Theory for Vigilant EIS , 1992, Inf. Syst. Res..

[32]  K. T. Veeramanju,et al.  Coherency identification using growing self organizing feature maps [power system stability] , 1998, Proceedings of EMPD '98. 1998 International Conference on Energy Management and Power Delivery (Cat. No.98EX137).

[33]  Hugh J. Watson,et al.  Tutorial: Business Intelligence - Past, Present, and Future , 2009, Communications of the Association for Information Systems.

[34]  Shirley Gregor,et al.  The Anatomy of a Design Theory , 2007, J. Assoc. Inf. Syst..

[35]  Surajit Chaudhuri,et al.  An overview of business intelligence technology , 2011, Commun. ACM.

[36]  Michel Verleysen,et al.  Forecasting electricity consumption using nonlinear projection and self-organizing maps , 2002, Neurocomputing.

[37]  Christian Rehtanz Visualisation of voltage stability in large electric power systems , 1999 .