Assessing the relevance of load profiling information in electrical load forecasting based on neural network models

International Journal of Electrical Power and Energy Systems

Volumn 40, Issue 1, 2012, Pages 85-93

  Sousa, J C ^a,c   Neves, L P ^a,c   Jorge, H M ^b,c  

^a POLYTECHNIC INSTITUTE OF LEIRIA   (Portugal)

^b UNIVERSITY OF COIMBRA   (Portugal)

^c INESC COIMBRA   (Portugal)

Author keywords

Load forecast; Load profiling; Neural networks; Sensitivity analysis

Indexed keywords

DISTRIBUTION FEEDERS; ELECTRICAL LOAD FORECASTING; INPUT SPACE; LOAD DIAGRAM; LOAD FORECAST; LOAD PROFILES; LOAD PROFILING; MEAN ABSOLUTE PERCENTAGE ERROR; NEURAL NETWORK MODEL; PARTIAL DERIVATIVES; REAL DISTRIBUTION; SMALL TOWNS;

ELECTRIC LOAD FORECASTING; ELECTRIC LOADS; NEURAL NETWORKS; SENSITIVITY ANALYSIS;

FORECASTING;

EID: 84860742606     PISSN: 01420615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijepes.2012.02.008     Document Type: Article

References (30)

1. H.L. Willis Power distribution planning reference book 2004 Marcel Dekker, Inc. New York p. 909-1054
2. Campbell P, Adamson K. Methodologies for load forecasting. In: Proceedings of the IEEE 3rd international conference on intelligent systems, London, UK, September 4-6; 2006. p. 800-6.
3. Chen H, Canizares CA, Singh A. ANN-based short-term load forecasting in electricity markets. In: Proceedings of the IEEE power engineering society winter meeting, Ohio, USA, January 28-February 1, vol. 2; 2001. p. 411-5.
4. H. Hippert, C. Pedreira, and R.C. Souza Neural networks for short-term load forecasting: a review and evaluation IEEE Trans Power Syst 16 1 2001 44 55 (Pubitemid 32297468)
5. A.S. AlFuhaid, M.A. El-Sayed, and M.S. Mahmoud Cascaded artificial neural networks for short-term load forecasting IEEE Trans Power Syst 12 4 1997 1524 1529 (Pubitemid 127771539)
6. P. Chang, C. Fan, and J. Lin Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network Int J Electr Power Energy Syst 33 1 2011 17 27
7. C. Xia, J. Wang, and McMenemy Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks Int J Electr Power Energy Syst 32 7 2010 743 750
8. D. Singhal, and K.S. Swarup Electricity price forecasting using artificial neural networks Int J Electr Power Energy Syst 33 3 2011 550 555
9. M.T. Hagan, H.B. Demuth, and M. Beale Neural network design 1996 PWS Publishing Company Boston MA, USA
10. Demuth H, Beale M, Hagan M. Neural Network Toolbox 5 User's Guide, Revised for Version 5.1. Natick MA, USA: Mathworks Inc. Copyright; 2007.
11. M.T. Hagan, and M.B. Menhaj Training feedforward networks with the marquardt algorithm IEEE Trans Neural Netw 5 6 1994 989 993
12. Ramezani M, Falaghi H, Haghifam M, Shahryari GA. Short-term electric load forecasting using neural networks. In: Proceedings of the EUROCON - international conference on computer as a tool; Belgrade, Serbia and Montenegro, vol. 2; 2005. p. 1525-8. (Pubitemid 46436909)
13. J.N. Fidalgo, and J.A. Peças Lopes Load forecasting performance enhancement when facing anomalous events IEEE Trans Power Syst 20 1 2005 408 415 (Pubitemid 40264513)
14. P. Santos, A. Martins, and A. Pires Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems Int J Electr Power Energy Syst 29 4 2007 338 347 (Pubitemid 46074135)
15. Chicco G, Napoli R, Piglione F. Load pattern clustering for short-term load forecasting of anomalous days. In: Proceedings of the IEEE PowerTech 2001, Porto, Portugal, September 10-13; 2001. p. 2.
16. Fidalgo J, Matos M. A. forecasting portugal global load with artificial neural networks. In: Proceedings of the ICANN2007 - international congress on artificial neural networks, Porto, Portugal, September 9-13; 2007. p. 728-37.
17. R. Lamedica, A. Prudenzi, M. Sforna, M. Caciotta, and V. Cencellli A neural network based technique for short-term forecasting of anomalous load periods IEEE Trans Power Syst 11 4 1996 1749 1756 (Pubitemid 126781339)
18. Guajardo J, Miranda J, Weber RA. Hybrid forecasting methodology using feature selection and support vector regression. In: Proceedings of the HIS '05 - fifth international conference on hybrid intelligent systems, Rio de Janeiro, Brasil, November 6-9; 2005. 6p.
19. J. Tikka Simultaneous input variable and basis function selection for RBF networks Elsevier Neurocomputing 72 10-12 2009 2649 2658
20. ERSE - Energy Services Regulatory Authority (2007), Guide of Data Measuring, Reading and Availability, document No. 4591-A/2007.
21. P. Jota, V. Silva, and F. Jota Building load management using cluster and statistical analyses Int J Electr Power Energy Syst 33 8 2011 1498 1505
22. D. Gerbec, S. Gasperic, I. Smon, and F. Gubina Allocation of the load profiles to consumers using probabilistic neural networks IEEE Trans Power Syst 20 2 2005 548 555 (Pubitemid 40699679)
23. Matos M, Fidalgo J, Ribeiro L. Deriving LV load diagrams for market purposes using commercial information. In: Proceedings of the 13th international conference on intelligent systems application to power systems, Virginia, USA, November 6-10; 2005. p. 105-10. (Pubitemid 46343388)
24. J. Jardini, C. Tahan, S. Ahn, and F.M. Figueiredo Daily load profiles for residential, commercial and industrial low voltages consumers IEEE Trans Power Deliv 15 1 2000 375 380
25. M. Espinoza, C. Joye, R. Belmans, and B. DeMoor Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series IEEE Trans Power Syst 20 3 2005 1622 1630 (Pubitemid 41300977)
26. G.J. Tsekouras, M.A. Tsaroucha, C.D. Tsirekis, A.D. Salis, E.N. Dialynas, and N.D. Hatziargyriou A database system for power systems customers and energy efficiency programs Int J Electr Power Energy Syst 33 6 2011 1220 1228
27. Sousa JC, Neves LP, Jorge HM. Integrating information available from load profiling in electric load forecasting. INESC Coimbra Research Report no. 17/2009. < http://www.inescc.pt/documentos/17-2009.pdf > [15.06.11].
28. Fidalgo JN. Feature subset selection based on ANN sensitivity analysis - a practical study. In: Proceedings of the WSES international conference on neural networks and applications, Tenerife, Spain, February 11-15; 2001. p. 206-11.
29. Sarle WS. How to measure importance of inputs. Cary, NC, USA: SAS Institute Inc. < ftp://ftp.sas.com/pub/neural/importance.html > [revised 23.06.2000] [at 6-May-2011].
30. J. Gama, and P.P. Rodrigues Electricity load forecast using data streams techniques A.R. Ganguly, J. Gama, O.A. Omitaomu, M.M. Gaber, R.R. Vatsavai, Knowledge discovery from sensor data 2008 CRC Press Florida 131 147