Skip to main content
SpringerLink
Log in

Database Systems for the Smart Grid

  • Chapter
  • First Online:
Book cover Smart Grids

Part of the book series: Green Energy and Technology ((GREEN))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Teradata Corporation. http://www.teradata.com

  2. Oracle Corporation. http://www.oracle.com

  3. SAS Institute, Inc. http://www.sas.com

  4. SAP AG http://www.sap.com

  5. IBM corporation. http://www.ibm.com

  6. Microsoft Corporation. http://www.microsoft.com

  7. Google Inc. http://www.google.com

  8. Farhangi H (2010) The path of the smart grid. IEEE Power Energy Mag 8:18–28

    Article  Google Scholar 

  9. Wikipedia: NoSQL (2013). http://en.wikipedia.org/wiki/NoSQL

  10. Apache Hadoop. http://hadoop.apache.org

  11. Wikipedia: MapReduce (2013). http://en.wikipedia.org/wiki/MapReduce

  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 183–188

    Google Scholar 

  13. Wikipedia: Cloud database (2013). http://en.wikipedia.org/wiki/Cloud_database

  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 250–253

    Google Scholar 

  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 2037–2044

    Google Scholar 

  16. Swartz RA, Lynch JP, Zerbst S, Sweetman B, Rolfes R (2010) Structural monitoring of wind turbines using wireless sensor networks. Smart Struct Syst 6:1–14

    Article  Google Scholar 

  17. Ben-Yaacov GZ (1979) Interactive computation and data management for power system studies. Comput J 22:76–79

    Article  Google Scholar 

  18. Papadakis M, Hatzjargyriou N, Gazidellis D (1989) Interactive data management system for power system planning studies. IEEE Trans Power Syst 4:329–335

    Article  Google Scholar 

  19. GE Power Controls. http://www.gepowercontrols.com

  20. Siemens Energy. http://www.energy.siemens.com

  21. Simpson, RH (2000) Power system database management. In: Conference record of 2000 annual pulp and paper industry technical conference (PPIC), pp 79–83

    Google Scholar 

  22. Wikipedia: Common information model (electricity) (2013) http://en.wikipedia.org/wiki/Common_Information_Model_(electricity)

  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 1–2

    Google Scholar 

  24. Wikipedia: Generic substation events (2013) http://en.wikipedia.org/wiki/Generic_Substation_Events

  25. Wikipedia: Substation configuration language (2013) http://en.wikipedia.org/wiki/Substation_Configuration_Language

  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 publishing

    Google Scholar 

  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 solutions

    Google Scholar 

  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 1883–1886

    Google Scholar 

  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. 1–12

    Google Scholar 

  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. 52–56

    Google Scholar 

  31. SISCO Inc: Integration of substation data. http://cimug.ucaiug.org/KB/Knowledge Base/Integration of Substation Data ver 06.pdf

  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 203–206

    Google Scholar 

  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 483–488

    Google Scholar 

  34. Wikipedia: Outage management system (2013) http://en.wikipedia.org/wiki/Outage_management_system

  35. Awerbuch S, Preston AM (1997) The virtual utility: accounting technology and competitive aspects of the emerging industry. Kluwer Academic Publisher

    Google Scholar 

  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. Wikipedia: Automatic meter reading (2013) http://en.wikipedia.org/wiki/Automatic_meter_reading

  38. Wikipedia: Advanced metering infrastructure (2013) http://en.wikipedia.org/wiki/Advanced_Metering_Infrastructure

  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 20–24

    Google Scholar 

  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 8:66–78

    Article  Google Scholar 

  41. IBM Software Group (2012) Managing big data for smart grids and smart meters. IBM Corporation, Tech. rep

    Google Scholar 

  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 285–290

    Google Scholar 

  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 726–729

    Google Scholar 

  44. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques. Morgan Kaufmann Publishers

    Google Scholar 

  45. Talia D, Trunfio P (2010) How distributed data mining tasks can thrive as knowledge services? Commun ACM 53:132–137

    Article  Google Scholar 

  46. Gama J (2010) Knowledge discovery from data streams. Chapman and Hall/CRC

    Google Scholar 

  47. Last M, Kandel A, Bunke H (2004) Data mining in time series databases. Word Scientific Press

    Google Scholar 

  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 294–299

    Google Scholar 

  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 36–54

    Google Scholar 

  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 175–191

    Google Scholar 

  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 46:1107–1118

    Article  Google Scholar 

  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 19

    Google Scholar 

  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 231–236

    Google Scholar 

  54. Hong T (2010) Short term electric load forecasting. Ph.D. thesis, North Carolina State University, USA

    Google Scholar 

  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 3200–3205

    Google Scholar 

  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 51–57

    Google Scholar 

  57. Taylor JW (2008) An evaluation of methods for very short term electricity demand forecasting using minute-by-minute British data. Int J Forecast 24:645–658

    Article  Google Scholar 

  58. Krishnaswamy S (2012) Energy analytics: when data mining meets the smart grid. http://smartgrid.i2r.a-star.edu.sg/2012/slides/i2r.pdf

  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 55:86–97

    Article  Google Scholar 

  60. Wikipedia: Dissolved gas analysis (2013) http://en.wikipedia.org/wiki/Dissolved_gas_analysis

  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 3:577–582

    Google Scholar 

  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 2141–2150

    Google Scholar 

  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 372–375

    Google Scholar 

  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 25:1427–1433

    Article  Google Scholar 

  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 26:2687–2696

    Article  Google Scholar 

  66. Calderaro V, Hadjicostis C, Piccolo A, Siano P (2011) Failure identification in smart grids based on Petri Net modeling. IEEE Trans Ind Electron 58:4613–4623

    Article  Google Scholar 

  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 22:164–171

    Article  MATH  Google Scholar 

  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 599–611

    Google Scholar 

  69. He H, Starzyk J (2006) A self-organizing learning array system for power quality classification based on wavelet transform. IEEE Trans Power Delivery 21:286–295

    Article  Google Scholar 

  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 390–393

    Google Scholar 

  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 1705–1711

    Google Scholar 

  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 33–34

    Google Scholar 

  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 23:1230–1243

    Article  Google Scholar 

  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 1–6

    Google Scholar 

  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. 1–8

    Google Scholar 

  76. Edwards RE, New J, Parker LE (2012) Predicting future hourly residential electrical consumption: a machine learning case study. Energy Buildings 49:591–603

    Article  Google Scholar 

  77. Chicco G, Napoli R, Postolache P, Scutariu M, Toader C (2003) Customer characterization options for improving the tariff offer. IEEE Trans Power Syst 18:381–387

    Article  Google Scholar 

  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 1–6

    Google Scholar 

  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 20:596–602

    Article  Google Scholar 

  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 1–8

    Google Scholar 

  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 96–111

    Google Scholar 

  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 1–6

    Google Scholar 

Download references

Acknowledgments

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”.

Author information

Authors and Affiliations

  1. Computing and Information Science Program, Masdar Institute of Science and Technology, Block 3, Masdar City, Abu Dhabi, 54224, United Arab Emirates

    Zeyar Aung

Authors
  1. Zeyar Aung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zeyar Aung .

Editor information

Editors and Affiliations

  1. , School of Information and Communication, CQ University, Bruce Highway, North Rockhampton, 4702, Queensland, Australia

    A B M Shawkat Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Aung, Z. (2013). Database Systems for the Smart Grid. In: Ali, A. (eds) Smart Grids. Green Energy and Technology. Springer, London. https://doi.org/10.1007/978-1-4471-5210-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-5210-1_7

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5209-5

  • Online ISBN: 978-1-4471-5210-1

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation