Smart Grids pp 151-168 | Cite as

Database Systems for the Smart Grid

  • Zeyar AungEmail author
Part of the Green Energy and Technology book series (GREEN)


In this chapter, two aspects of database systems, namely database management and data mining, for the smart grid are covered. The uses of database management and data mining for the electrical power grid comprising of the interrelated subsystems of power generation, transmission, distribution, and utilization are discussed.


Support Vector Regression Smart Grid Association Rule Mining Load Forecast Local Outlier Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author thank the Government and Abu Dhabi, United Arab Emirates, for sponsoring this research through its funding of Masdar Institute–Massachusetts Institute of Technology (MIT) collaborative research project titled “Data Mining for Smart Grids”.


  1. 1.
    Teradata Corporation.
  2. 2.
    Oracle Corporation.
  3. 3.
    SAS Institute, Inc.
  4. 4.
  5. 5.
    IBM corporation.
  6. 6.
    Microsoft Corporation.
  7. 7.
  8. 8.
    Farhangi H (2010) The path of the smart grid. IEEE Power Energy Mag 8:18–28CrossRefGoogle Scholar
  9. 9.
    Wikipedia: NoSQL (2013).
  10. 10.
    Apache Hadoop.
  11. 11.
    Wikipedia: MapReduce (2013).
  12. 12.
    Rizvi SS, Chung TS (2010) Flash memory SSD based DBMS for high performance computing embedded and multimedia systems. In: Proceedings of the 2010 international conference on computer engineering and systems (ICCES), pp 183–188Google Scholar
  13. 13.
    Wikipedia: Cloud database (2013).
  14. 14.
    Li Jq, Wang Sl, Niu Cl, Liu Jz (2008) Research and application of data mining technique in power plant. In: Proceedings of the 2008 international symposium on computational intelligence and design (ISCID), vol 2, pp 250–253Google Scholar
  15. 15.
    Huang, JA, Vanier G, Valette A, Harrison S, Wehenkel L (2003) Application of data mining techniques for automat settings in emergency control at Hydro-Quebec. In: Proceedings of the 2003 IEEE power engineering society general meeting, vol 4, pp 2037–2044Google Scholar
  16. 16.
    Swartz RA, Lynch JP, Zerbst S, Sweetman B, Rolfes R (2010) Structural monitoring of wind turbines using wireless sensor networks. Smart Struct Syst 6:1–14CrossRefGoogle Scholar
  17. 17.
    Ben-Yaacov GZ (1979) Interactive computation and data management for power system studies. Comput J 22:76–79CrossRefGoogle Scholar
  18. 18.
    Papadakis M, Hatzjargyriou N, Gazidellis D (1989) Interactive data management system for power system planning studies. IEEE Trans Power Syst 4:329–335CrossRefGoogle Scholar
  19. 19.
    GE Power Controls.
  20. 20.
  21. 21.
    Simpson, RH (2000) Power system database management. In: Conference record of 2000 annual pulp and paper industry technical conference (PPIC), pp 79–83Google Scholar
  22. 22.
    Wikipedia: Common information model (electricity) (2013)
  23. 23.
    Simmins JJ (2011) The impact of PAP 8 on the Common Information Model (CIM). In: Proceedings of the 2011 IEEE/PES power systems conference and exposition (PSCE), pp 1–2Google Scholar
  24. 24.
    Wikipedia: Generic substation events (2013)
  25. 25.
    Wikipedia: Substation configuration language (2013)
  26. 26.
    Morais J, Pires Y, Cardoso C, Klautau A (2009) An overview of data mining techniques applied to power systems. In: Ponce J, Karahoca A (eds) Data mining and knowledge discovery in real life applications. I-Tech education and publishingGoogle Scholar
  27. 27.
    Martinez C, Huang H, Guttromson R (2005) Archiving and management of power systems data for real-time performance monitoring platform. Tech. rep, Consortium of electric reliability technology solutionsGoogle Scholar
  28. 28.
    Qiu J, Liu J, Hou Y, Zhang J (2011) Use of real-time/historical database in smart grid. In: Proceedings of the 2011 international conference on electric information and control engineering (ICEICE), pp 1883–1886Google Scholar
  29. 29.
    Owoola MA (2004) A generic spatial database schema for a typical electric transmission utility. In: Proceedings of the geospatial information and technology association’s 27th Annual Conference (GITA), pp. 1–12Google Scholar
  30. 30.
    Lu B, Song W (2010) Research on heterogeneous data integration for smart grid. In: Proceedings of the 2010 3rd IEEE international conference on computer science and information technology (ICCSIT), vol 3, pp. 52–56Google Scholar
  31. 31.
  32. 32.
    Zheng L, Chen S, Hu Y, He J (2011) Applications of cloud computing in the smart grid. In: Proceedings of the 2nd international conference on artificial intelligence, management science and electronic commerce (AIMSEC), pp 203–206Google Scholar
  33. 33.
    Rusitschka S, Eger K, Gerdes C (2010) Smart grid data cloud: a model for utilizing cloud computing in the smart grid domain. In: Proceedings of the 1st IEEE international conference on smart grid communications (SmartGridComm), pp 483–488Google Scholar
  34. 34.
    Wikipedia: Outage management system (2013)
  35. 35.
    Awerbuch S, Preston AM (1997) The virtual utility: accounting technology and competitive aspects of the emerging industry. Kluwer Academic PublisherGoogle Scholar
  36. 36.
    Kaplan SM, Sissine F, Abel A, Wellinghoff J, Kelly SG, Hoecker JJ (2009) Smart grid: modernizing electric power transmission and distribution; energy independence, storage and security; energy independence and security Act of 2007 (EISA); Improving electrical grid efficiency, communication, reliability, and resiliency; integrating new and renewable energy sources. TheCapitol.Net, Inc.Google Scholar
  37. 37.
    Wikipedia: Automatic meter reading (2013)
  38. 38.
    Wikipedia: Advanced metering infrastructure (2013)
  39. 39.
    Hart DG (2008) Using AMI to realize the smart grid. In: Proceedings of the conference on power and energy society general meeting - conversion and delivery of electrical energy in the 21st Century, pp 20–24Google Scholar
  40. 40.
    Lui TJ, Stirling W, Marcy HO (2010) Get smart: using demand response with appliances to cut peak energy use, drive energy conservation, enable renewable energy sources and reduce greenhouse-gas emissions. IEEE Power Energy Mag 8:66–78CrossRefGoogle Scholar
  41. 41.
    IBM Software Group (2012) Managing big data for smart grids and smart meters. IBM Corporation, Tech. repGoogle Scholar
  42. 42.
    Arenas-Martinez M, Herrero-Lopez S, Sanchez A, Williams JR, Roth P, Hofmann P, Zeier A (2010) A comparative study of data storage and processing architectures for the smart grid. In: Proceedings of the 1st IEEE international conference on smart grid communications (SmartGridComm), pp 285–290Google Scholar
  43. 43.
    Fan Z (2011) Distributed demand response and user adaptation in smart grids. In: Proceedings of the 2011 IFIP/IEEE international symposium on integrated network management (IM), pp 726–729Google Scholar
  44. 44.
    Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques. Morgan Kaufmann PublishersGoogle Scholar
  45. 45.
    Talia D, Trunfio P (2010) How distributed data mining tasks can thrive as knowledge services? Commun ACM 53:132–137CrossRefGoogle Scholar
  46. 46.
    Gama J (2010) Knowledge discovery from data streams. Chapman and Hall/CRCGoogle Scholar
  47. 47.
    Last M, Kandel A, Bunke H (2004) Data mining in time series databases. Word Scientific PressGoogle Scholar
  48. 48.
    Li D, Aung Z, Williams J, Sanchez A (2012) P3: privacy preservation protocol for appliance control application. In: Proceedings of the 3rd IEEE international conference on smart grid communications (SmartGridComm), pp 294–299Google Scholar
  49. 49.
    Lindell Y, Pinkas B (2000) Privacy preserving data mining. In: Proceedings of the 20th annual international cryptology conference on advances in cryptology (CRYPTO), pp 36–54Google Scholar
  50. 50.
    Kursawe K, Danezis G, Kohlweiss M (2011) Privacy-friendly aggregation for the smart-grid. In: Proceedings of the 11th international symposium on privacy enhancing technologies (PETS), pp 175–191Google Scholar
  51. 51.
    Li DHW, Cheung GHW, Lam JC (2005) Analysis of the operational performance and efficiency characteristic for photovoltaic system in Hong Kong. Energy Convers Manag 46:1107–1118CrossRefGoogle Scholar
  52. 52.
    Fan S, Chen L, Lee W (2008) Short-term load forecasting using comprehensive combination based on multi-meteorological information. In: Proceedings of the 2008 IEEE/IAS industrial and commercial power systems technical conference (ICPS), pp 1–7 19Google Scholar
  53. 53.
    Deng J, Jirutitijaroen P (2010) Short-term load forecasting using time series analysis: a case study for Singapore. In: Proceedings of the 2010 IEEE conference on cybernetics and intelligent systems (CIS), pp 231–236Google Scholar
  54. 54.
    Hong T (2010) Short term electric load forecasting. Ph.D. thesis, North Carolina State University, USAGoogle Scholar
  55. 55.
    Zhang HT, Xu FY, Zhou L (2010) Artificial neural network for load forecasting in smart grid. In: Proceedings of the 2010 international conference on machine learning and cybernetics (ICMLC), vol 6, pp 3200–3205Google Scholar
  56. 56.
    Aung Z, Toukhy M, Williams J, Sanchez A, Herrero S (2012) Towards accurate electricity load forecasting in smart grids. In: Proceedings of the 4th international conference on advances in databases, knowledge, and data applications (DBKDA), pp 51–57Google Scholar
  57. 57.
    Taylor JW (2008) An evaluation of methods for very short term electricity demand forecasting using minute-by-minute British data. Int J Forecast 24:645–658CrossRefGoogle Scholar
  58. 58.
    Krishnaswamy S (2012) Energy analytics: when data mining meets the smart grid.
  59. 59.
    Ramchurn SD, Vytelingum P, Rogers A, Jennings NR (2012) Putting the “smarts” into the smart grid: a grand challenge for artificial intelligence. Commun ACM 55:86–97CrossRefGoogle Scholar
  60. 60.
    Wikipedia: Dissolved gas analysis (2013)
  61. 61.
    Sharma NK, Tiwari PK, Sood YR (2011) Review of artificial intelligence techniques application to dissolved gas analysis on power transformer. Int J Comput Electr Eng 3:577–582Google Scholar
  62. 62.
    Chen Y, Huang Z, Liu Y, Rice MJ, Jin S (2012) Computational challenges for power system operation. In: Proceedings of the 2012 Hawaii international conference on system sciences (HICSS) pp 2141–2150Google Scholar
  63. 63.
    Zhong W, Sun Y, Xu M, Liu J (2010) State assessment system of power transformer equipments based on data mining and fuzzy theory. In: Proceedings of the 2010 international conference on intelligent computation technology and automation (ICICTA), vol 3, pp 372–375Google Scholar
  64. 64.
    Samantaray SR, El-Arroudi K, Joós G, Kamwa I (2010) A fuzzy rule-based approach for islanding detection in distributed generation. IEEE Trans Power Delivery 25:1427–1433CrossRefGoogle Scholar
  65. 65.
    Najy W, Zeineldin H, Alaboudy AK, Woon WL (2011) A Bayesian passive islanding detection method for inverter-based distributed generation using ESPRIT. IEEE Trans Power Delivery 26:2687–2696CrossRefGoogle Scholar
  66. 66.
    Calderaro V, Hadjicostis C, Piccolo A, Siano P (2011) Failure identification in smart grids based on Petri Net modeling. IEEE Trans Ind Electron 58:4613–4623CrossRefGoogle Scholar
  67. 67.
    Xu L, Chow MY, Taylor LS (2007) Power distribution fault cause identification with imbalanced data using the data mining-based fuzzy classification E-algorithm. IEEE Trans Power Syst 22:164–171zbMATHCrossRefGoogle Scholar
  68. 68.
    Adolf R, Haglin D, Halappanavar M, Chen Y, Huang Z (2011) Techniques for improving filters in power grid contingency analysis. In: Proceedings of the 7th international conference on machine learning and data mining in pattern recognition (MLDM), pp 599–611Google Scholar
  69. 69.
    He H, Starzyk J (2006) A self-organizing learning array system for power quality classification based on wavelet transform. IEEE Trans Power Delivery 21:286–295CrossRefGoogle Scholar
  70. 70.
    Hongke H, Linhai Q (2010) Application and research of multidimensional data analysis in power quality. In: Proceedings of the 2010 international conference on computer design and applications (ICCDA), vol 1, pp 390–393Google Scholar
  71. 71.
    Gross P, Boulanger A, Arias M, Waltz D, Long PM, Lawson C, Anderson R, Koenig M, Mastrocinque M, Fairechio W, Johnson JA, Lee S, Doherty F, Kressner A (2006) Predicting electricity distribution feeder failures using machine learning susceptibility analysis. In: Proceedings of the 18th conference on innovative applications of artificial intelligence (IAAI), vol 2, pp 1705–1711Google Scholar
  72. 72.
    Mori H (2006) State-of-the-art overview on data mining in power systems. In: Proceedings of the 2006 IEEE PES power systems conference and exposition (PSCE), pp 33–34Google Scholar
  73. 73.
    Martínez-Álvarez F, Troncoso A, Riquelme JC, Aguilar-Ruiz JS (2011) Energy time series forecasting based on pattern sequence similarity. IEEE Trans Knowl Data Eng 23:1230–1243CrossRefGoogle Scholar
  74. 74.
    Neupane B, Perera KS, Aung Z, Woon WL (2012) Artificial neural network-based electricity price forecasting for smart grid deployment. In: Proceedings of the 2012 IEEE international conference on computer systems and industrial informatics (ICCSII), pp 1–6Google Scholar
  75. 75.
    Fernandez I, Borges CE, Penya YK (2011) Efficient building load forecasting. In: Proceedings of the 16th IEEE conference on emerging technologies and factory automation (ETFA), pp. 1–8Google Scholar
  76. 76.
    Edwards RE, New J, Parker LE (2012) Predicting future hourly residential electrical consumption: a machine learning case study. Energy Buildings 49:591–603CrossRefGoogle Scholar
  77. 77.
    Chicco G, Napoli R, Postolache P, Scutariu M, Toader C (2003) Customer characterization options for improving the tariff offer. IEEE Trans Power Syst 18:381–387CrossRefGoogle Scholar
  78. 78.
    Fernandes RAS, Silva IN, Oleskovicz M (2010) Identification of residential load profile in the Smart Grid context. In: Proceedings of the 2010 IEEE power and energy society general meeting, pp 1–6Google Scholar
  79. 79.
    Figueiredo V, Rodrigues F, Vale Z, Gouveia JB (2005) An electric energy consumer characterization framework based on data mining techniques. IEEE Trans Power Syst 20:596–602CrossRefGoogle Scholar
  80. 80.
    Li D, Aung Z, Williams J, Sanchez A (2012) Efficient authentication scheme for data aggregation in smart grid with fault tolerance and fault diagnosis. In: Proceedings of the 2012 IEEE PES conference on innovative smart grid technologies (ISGT), pp 1–8Google Scholar
  81. 81.
    Faisal MA, Aung Z, Williams JR, Sanchez A (2012) Securing advanced metering infrastructure using intrusion detection system with data stream mining. In: Proceedings of the 2012 Pacific Asia workshop on intelligence and security informatics (PAISI), pp 96–111Google Scholar
  82. 82.
    Fatemieh O, Chandra R, Gunter CA (2010) Low cost and secure smart meter communications using the TV white spaces. In: Proceedings of the 2010 IEEE international symposium on resilient control systems (ISRCS), pp 1–6Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Computing and Information Science ProgramMasdar Institute of Science and TechnologyMasdar CityUnited Arab Emirates

Personalised recommendations