Table of Contents
Advances in Electrical Engineering
Volume 2014 (2014), Article ID 865621, 10 pages
http://dx.doi.org/10.1155/2014/865621
Research Article

Assessment of Electrical Load in Water Distribution Systems Using Representative Load Profiles-Based Method

Power System Department, Electrical Engineering Faculty, “Gheorghe Asachi” Technical University of Iasi, Boulevard Dimitrie Mangeron, No. 21-23, 700050 Iasi, Romania

Received 17 April 2014; Accepted 28 June 2014; Published 21 July 2014

Academic Editor: Mamun B. Ibne Reaz

Copyright © 2014 Gheorghe Grigoras. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Alliance to Save Energy, “What watergy involves,” 2014, http://www.watergy.net.
  2. P. Boulos, Z. Wu, C. Orr, M. Moore, P. Hsiung, and D. Thomas, Optimal Pump Operation of Water Distribution Systems Using Genetic Algorithms, H2ONET-Users Guide, MW Software Consulting, Dallas County, Tex, USA, 2000.
  3. European Commission, Directorate General for Environment, 2013, http://ec.europa.eu/environment/water/ index_en.htm.
  4. A. Anton and S. Perju, “Monitoring the main parameters of a water supply pumping station over ten years,” in Proceedings of the 6th International Conference on Hydraulic Machinery and Hydrodynamics, Timisoara, Romania, 2004.
  5. S. J. Kenway, A. Priestley, S. Cook et al., “Energy use in the provision and consumption of urban water in Australia and New Zealand,” Water for a Healthy Country National Research Flagship Report, CSIRO, 2008. View at Google Scholar
  6. L. House, Water Supply Related Electricity Demand in California, Report for California Energy Commission, 2006.
  7. R. Goldstein and W. Smith, “Water and sustainability: U.S. electricity consumption for water supply & treatment—the next half century,” EPRI Report, Electric Power Research Institute, Palo Alto, Calif, USA, 2000. View at Google Scholar
  8. I. Pulido-Calvo and J. C. Gutiérrez-Estrada, “Selection and operation of pumping stations of water distribution systems,” Environmental Research Journal, vol. 5, no. 3, pp. 1–20, 2011. View at Google Scholar
  9. K. Feldman, “Aspects of energy efficiency in water distribution systems,” in Proceedings of the International Conference on Water Loss, pp. 26–30, IWA, Cape Town, South Africa, April 2009.
  10. G. C. Ionescu and D. S. Ionescu, “The optimization of energy consumption in water supply systems,” Acta Electrotehnica, vol. 46, pp. 191–194, 2005. View at Google Scholar
  11. M. Istrate and G. Grigoraş, “Energy consumption estimation in water distribution systems using fuzzy techniques,” Environmental Engineering and Management Journal, vol. 9, no. 2, pp. 249–256, 2010. View at Google Scholar · View at Scopus
  12. I. Sârbu, Energetic Optimization of the Water Distribution Systems, Publishing House of the Romanian Academy, Bucharest, Romania, 1997.
  13. L. Szychta, “Energy consumption of water pumping for selected control systems,” Electrical Power Quality and Utilization Journal, vol. 12, pp. 21–27, 2006. View at Google Scholar
  14. G. Grigoras and M. Istrate, “An efficient clustering method in evaluation of the electric energy consumption from water hydrophore stations,” International Review on Modelling and Simulations, vol. 4, no. 2, pp. 813–818, 2011. View at Google Scholar · View at Scopus
  15. U. Zessler and U. Shamir, “Optimal operation of water distribution systems,” Journal of Water Resources Planning and Management, vol. 115, no. 6, pp. 735–752, 1989. View at Publisher · View at Google Scholar · View at Scopus
  16. P. Cutore, A. Campisano, Z. Kapelan, C. Modica, and D. Savic, “Probabilistic prediction of urban water consumption using the SCEM-UA algorithm,” Urban Water Journal, vol. 5, no. 2, pp. 125–132, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. G. Chicco, “Overview and performance assessment of the clustering methods for electrical load pattern grouping,” Energy, vol. 42, no. 1, pp. 68–80, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,” http://dataclustering.cse.msu.edu/papers/MSU-CSE-00-16.pdf.
  19. R. Siddheswar and R. Turi, “Determination of number of clusters in K-Means clustering and application in color image segmentation,” in Proceedings of the 4th International Conference on Advanced in Pattern Recognition and Digital Techniques, Calcutta, India, 1999.
  20. I. Yatskiv and L. Gusarova, “The methods of cluster analysis results validation,” in Proceedings of International Conference RelStat'04, Riga, Latvia, 2004.
  21. J. Yu and Q. Cheng, “The upper bound of the optimal number of clusters in fuzzy clustering,” Science in China Series F, vol. 44, pp. 119–124, 2001. View at Google Scholar
  22. “A tutorial on clustering algorithms,” http://home.dei.polimi.it/matteucc/Clustering/tutorial_html.
  23. P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65, 1987. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Halkidi, Y. Batistakis, and M. Vazirgiannis, “On clustering validation techniques,” Journal of Intelligent Information Systems, vol. 17, no. 2-3, pp. 107–145, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  25. M. R. Rezaee, B. P. F. Lelieveldt, and J. H. C. Reiber, “A new cluster validity index for the fuzzy C-mean,” Pattern Recognition Letters, vol. 19, no. 3-4, pp. 237–246, 1998. View at Publisher · View at Google Scholar · View at Scopus
  26. P. Berkhin, “Survey of clustering data mining techniques,” Tech. Rep., Accrue Software, San Jose, Calif, USA, 2002. View at Google Scholar
  27. M. Holgersson, “The limited value of cophenetic correlation as a clustering criterion,” Pattern Recognition, vol. 10, no. 4, pp. 287–295, 1978. View at Publisher · View at Google Scholar · View at Scopus
  28. A. D. Gordon, Classification, Chapman & Hall, New York, NY, USA, 2nd edition, 1999.
  29. C. G. Carter-Brown, “Load profile modeling for integrated energy planning,” in Proceedings of the Domestic Use of Electrical Energy Conference, pp. 13–18, Cape Town, South Africa, April 1999.
  30. British Electricity Boards, “Report on the design of low voltage underground networks for new housing,” ACE Report No. 105, 1986. View at Google Scholar
  31. British Electricity Boards, “Report on the computer program DEBUTE for the design of LV radial networks. Part 1—general considerations , part 2—program user guide,” Tech. Rep. 115, 1988. View at Google Scholar
  32. A. Seppälä, Load research and load estimation in electricity distribution [Ph.D. thesis], Helsinki University of Technology, Esbo, Finland, 1996.
  33. J. Nazarko and Z. A. Styczynski, “Application of statistical and neural approaches to the daily load profiles modelling in power distribution systems,” in Proceedings of the IEEE/PES Transmission and Distribution Conference, pp. 320–325, New Orleans, La, USA, May 1999. View at Scopus
  34. Z. Zakaria, M. N. Othman, and M. H. Sohod, “Consumer load profiling using fuzzy clustering and statistical approach,” in Proceedings of the 4th Student Conference on Research and Development (SCOReD '06), 274, p. 270, IEEE, Selangor, Malaysia, June 2006. View at Publisher · View at Google Scholar
  35. I. H. Yu, J. K. Lee, J. M. Ko, and S. I. Kim, “A method for classification of electricity demands using load profile data,” in Proceedings of the 4th Annual ACIS International Conference on Computer and Information Science (ICIS '05), pp. 164–168, Jeju Island, South Korea, July 2006. View at Scopus
  36. G. Chicco, R. Napoli, and F. Piglione, “Comparisons among clustering techniques for electricity customer classification,” IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 933–940, 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. G. Chicco, R. Napoli, and F. Piglione, “Application of clustering algorithms and self organising maps to classify electricity customers,” in Proceedings of the IEEE Bologna PowerTech Conference, vol. 1, Bologna, Italy,, June 2003. View at Publisher · View at Google Scholar · View at Scopus
  38. D. Gerbek, S. Gasperic, and F. Gubina, “Comparison of different classification methods for the consumers' load profile determination,” in Proceedings of the 17th International Conference on Electricity Distribution, Barcelona, Spain, 2003.
  39. G. Grigoras, M. Istrate, and F. Scarlatache, “Electrical energy consumption estimation in water distribution systems using a clustering based method,” in Proceedings of the International Conference on Electronics, Computers and Artificial Intelligence (ECAI '13), pp. 1–6, Pitesti, Romania, June 2013. View at Publisher · View at Google Scholar
  40. N. Mahmoudi-Kohan, M. P. Moghaddam, and S. M. Bidaki, “Evaluating performance of WFA K-means and modified follow the leader methods for clustering load curves,” in Proceedings of the IEEE/PES Power Systems Conference and Exposition (PSCE '09), Seatle, Wash, USA, March 2009. View at Publisher · View at Google Scholar · View at Scopus
  41. G. Grigoras, M. Istrate, and F. Scarlatache, “Electrical energy consumption estimation in water distribution systems using a clustering based method,” in Proceedings of the International Conference on Electronics, Computers and Artificial Intelligence (ECAI '13), pp. 1–6, Pitesti, Romania, June 2013. View at Publisher · View at Google Scholar
  42. A. Mutanen, M. Ruska, S. Repo, and P. Järventausta, “Customer classification and load profiling method for distribution systems,” IEEE Transactions on Power Delivery, vol. 26, no. 3, pp. 1755–1763, 2011. View at Publisher · View at Google Scholar · View at Scopus
  43. G. Grigoras, C. Barbulescu, G. Cartina, and D. Comanescu, “A comparative study regarding efficiency of the hierarchical clustering techniques in typical load profiles determination,” Acta Electrotehnica, vol. 52, no. 5, pp. 184–188, 2011. View at Google Scholar
  44. B. D. Pitt and D. S. Kirschen, “Application of data mining techniques to load profiling,” in Proceedings of the 21st IEEE International Conference Power Industry Computer Applications, Santa Clara, Calif, USA, 1999.
  45. S. Ramos and Z. Vale, “Data mining techniques application in power distribution utilities,” in Proceedings of the IEEE/PES Transmission and Distribution Conference and Exposition, pp. 1–8, Chicago, Ill, USA, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. V. Figueiredo, F. Rodrigues, Z. Vale, and J. B. Gouveia, “An electric energy consumer characterization framework based on data mining techniques,” IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 596–602, 2005. View at Publisher · View at Google Scholar · View at Scopus
  47. A. H. Nizar, Z. Y. Dong, and J. H. Zhao, “Load profiling and data mining techniques in electricity deregulated market,” in Proceedings of the IEEE Power Engineering Society General Meeting (PES '06), Montreal, Canada, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  48. G. J. Tsekouras, N. D. Hatziargyriou, and E. N. Dialynas, “Two-stage pattern recognition of load curves for classification of electricity customers,” IEEE Transactions on Power Systems, vol. 22, no. 3, pp. 1120–1128, 2007. View at Publisher · View at Google Scholar · View at Scopus
  49. S. V. Verdú, M. O. García, F. J. G. Franco et al., “Characterization and identification of electrical customers through the use of self-organizing maps and daily load parameters,” in Proceedings of the IEEE PES Power System Conference and Exposition, vol. 2, pp. 899–906, Atlanta, Ga, USA, 2004. View at Publisher · View at Google Scholar
  50. K. L. Lo and Z. Zakaria, “Electricity consumer classification using artificial intelligence,” in Proceedings of the 39th International Universities Power Engineering Conference, Bristol, UK, 2004.
  51. D. Gerbec, S. Gašperič, I. Šmon, and F. Gubina, “Determining the load profiles of consumers based on fuzzy logic and probability neural networks,” IEE Proceedings: Generation, Transmission and Distribution, vol. 151, no. 3, pp. 395–400, 2004. View at Publisher · View at Google Scholar · View at Scopus
  52. D. Gerbec, S. Gašperič, I. Šmon, and F. Gubina, “Allocation of the load profiles to consumers using probabilistic neural networks,” IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 548–555, 2005. View at Publisher · View at Google Scholar · View at Scopus
  53. M. Sarlak, T. Ebrahimi, and S. S. Karimi Madahi, “Enhancement the accuracy of daily and hourly short time load forecasting using neural network,” Journal of Basic and Applied Scientific Research, vol. 2, no. 1, pp. 247–255, 2012. View at Google Scholar
  54. H. K. Alfares and M. Nazeeruddin, “Electric load forecasting: Literature survey and classification of methods,” International Journal of Systems Science, vol. 33, no. 1, pp. 23–34, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  55. L. A. Garcia-Escudero and A. Gordaliza, “A proposal for robust curve clustering,” Journal of Classification, vol. 22, no. 2, pp. 185–201, 2005. View at Publisher · View at Google Scholar · View at Scopus
  56. Z. Zisman and G. Cartina, “Application of fuzzy logic for distribution system estimation,” in Proceedings of the 22nd Seminar on Fundamentals of Electrotechnics and Circuit Theory, Ustroń, Poland, May 1999.
  57. L. Jentgen, H. Kidder, R. Hill, and S. Conrad, Water Consumption Forecasting to Improve Energy Efficiency of Pumping Operations, Awwa Research Foundation, 2007.