A new two-stage approach to short term electrical load forecasting

Energies

Volumn 6, Issue 4, 2013, Pages 2130-2148

  Božić, Miloš ^a   Stojanović, Miloš ^b   Stajić, Zoran ^a   Tasić, Dragan ^a  

^a UNIVERSITY OF NI   (Serbia)

^b Higher Technical School of Professional Education   (Serbia)

Author keywords

Average daily load; Least squares support vector machines; Short term load forecasting; Two stage approach

Indexed keywords

COMMERCE; DECISION MAKING; DEREGULATION; ELECTRIC POWER PLANT LOADS; FORECASTING; POWER MARKETS; SUPPORT VECTOR MACHINES;

DEREGULATED ENERGY MARKET; HOURLY LOAD FORECASTING; LEAST SQUARES SUPPORT VECTOR MACHINES; NONSTATIONARY PROCESS; OPERATIONAL DECISION MAKING; SHORT TERM LOAD FORECASTING; SHORT-TERM ELECTRICAL LOADS; TWO STAGE APPROACH;

ELECTRIC LOAD FORECASTING;

EID: 84877349401     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en6042130     Document Type: Article

References (30)

1. Soliman, S.A.; Alkandari, A.M. Electrical Load Forecasting: Modeling and Model Construction; Butterworth-Heinemann: Burlington, MA, USA, 2010.
2. Papalexopoulos, A.D.; Hesterberg, T.C. A regression-based approach to short-term system load forecasting. IEEE Trans. Power Syst. 1990, 5, 1535-1547.
3. Christiaanse, W.R. Short-term load forecasting using general exponential smoothing. IEEE Trans. Power Appar. Syst. 1971, PAS-90, 900-911.
4. Vähäkyla, P.; Hakonen, E.; Léman, P. Short-term forecasting of grid load using Box-Jenkins techniques. Int. J. Electr. Power Energy Syst. 1980, 2, 29-34.
5. Irisarri, G.D.; Widergren, S.E.; Yehsakul, P.D. On-line load forecasting for energy control center application. IEEE Power Eng. Rev. 1982, PAS-101, 71-78.
6. Mori, H.; Kobayashi, H. Optimal fuzzy inference for short-term load forecasting. IEEE Trans. Power Syst. 1996, 11, 390-396.
7. Ranaweera, D.K.; Hubele, N.F.; Karady, G.G. Fuzzy logic for short term load forecasting. Int. J. Electr. Power Energy Syst. 1996, 18, 215-222.
8. Rahman, S.; Bhatnagar, R. An expert system based algorithm for short term load forecast. IEEE Trans. Power Syst. 1988, 3, 392-399.
9. Dillon, T.S.; Sestito, S.; Leung, S. Short term load forecasting using an adaptive neural network. Int. J. Electr. Power Energy Syst. 1991, 13, 186-192.
10. Hippert, H.S.; Pedreira, C.E.; Souza, R.C. Neural networks for short-term load forecasting: A review and evaluation. IEEE Trans. Power Syst. 2001, 16, 44-55.
11. Chen, B.-J.; Chang, M.-W.; Lin, C.-J. Load forecasting using support vector Machines: A study on EUNITE competition 2001. IEEE Trans. Power Syst. 2004, 19, 1821-1830.
12. Hong, W.-C. Electric load forecasting by support vector model. Appl. Math. Model. 2009, 33, 2444-2454.
13. Fan, S.; Chen, L. Short-term load forecasting based on an adaptive hybrid method. IEEE Trans. Power Syst. 2006, 21, 392-401.
14. Amjady, N.; Keynia, F. Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm. Energy 2009, 34, 46-57.
15. Wang, J.; Zhu, S.; Zhang, W.; Lu, H. Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy 2010, 35, 1671-1678.
16. Hong, W.-C. Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm. Energy 2011, 36, 5568-5578.
17. Borges, C.; Penya, Y.; Fernandez, I. Evaluating combined load forecasting in large power systems and smart grids. IEEE Trans. Ind. Inf. 2013, doi:10.1109/TII.2012.2219063.
18. Taylor, J.W. Short-term load forecasting with exponentially weighted methods. IEEE Trans. Power Syst. 2012, 27, 458-464.
19. Mao, H.; Zeng, X.-J.; Leng, G.; Zhai, Y.-J.; Keane, J.A. Short-term and midterm load forecasting using a bilevel optimization model. IEEE Trans. Power Syst. 2009, 24, 1080-1090.
20. Kebriaei, H.; Araabi, B.N.; Rahimi-Kian, A. Short-term load forecasting with a new nonsymmetric penalty function. IEEE Trans. Power Syst. 2011, 26, 1817-1825.
21. Nose-Filho, K.; Lotufo, A.D.P.; Minussi, C.R. Short-term multinodal load forecasting using a modified general regression neural network. IEEE Trans. Power Deliv. 2011, 26, 2862-2869.
22. Kandil, N.; Wamkeue, R.; Saad, M.; Georges, S. An efficient approach for short term load forecasting using artificial neural networks. Int. J. Electr. Power Energy Syst. 2006, 28, 525-530.
23. Soares, L.J.; Medeiros, M.C. Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data. Int. J. Forecast. 2008, 24, 630-644.
24. Niu, D.; Wang, Y.; Wu, D.D. Power load forecasting using support vector machine and ant colony optimization. Expert Syst. Appl. 2010, 37, 2531-2539.
25. Kelo, S.M.; Dudul, S.V. Short-term Maharashtra state electrical power load prediction with special emphasis on seasonal changes using a novel focused time lagged recurrent neural network based on time delay neural network model. Expert Syst. Appl. 2011, 38, 1554-1564.
26. Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn 1995, 20, 273-297.
27. Suykens, J.A.K.; Gestel, T.V.; Brabanter, J.D.; Moor, B.D.; Vandewalle, J. Least Squares Support Vector Machines; World Scientific Publishing Company: Singapore, 2002.
28. ISO New England Historical Data. Available online: http://www.iso-ne.com/markets/hst_rpts/hstRpts.do?category=Hourly (accessed on 12 April 2013).
29. Taylor, J.W.; de Menezes, L.M.; McSharry, P.E. A comparison of univariate methods for forecasting electricity demand up to a day ahead. Int. J. Forecast. 2006, 22, 1-16.
30. Taylor, J.W. Short-term electricity demand forecasting using double seasonal exponential smoothing. J. Oper. Res. Soc. 2003, 54, 799-805