Short-term ANN load forecasting from limited data using generalization learning strategies

Neurocomputing

Volumn 70, Issue 1-3, 2006, Pages 409-419

  Chan, Zeke S H ^a   Ngan, H W ^b   Rad, A B ^b   David, A K ^b   Kasabov, N ^a  

^a AUCKLAND UNIVERSITY OF TECHNOLOGY   (New Zealand)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Day ahead forecasting; Genetic algorithm; Open electricity market; Regularization

Indexed keywords

ELECTRIC LOAD FORECASTING; ERROR DETECTION; GENETIC ALGORITHMS; LEARNING SYSTEMS;

DAY-AHEAD LOAD FORECASTING; GENERALIZATION LEARNING TECHNIQUES; OPEN ELECTRICITY MARKET; STANDARD ERROR MINIMIZATION METHOD;

MULTILAYER NEURAL NETWORKS;

ANALYTIC METHOD; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; COMMERCIAL PHENOMENA; CONTROLLED STUDY; ELECTRICITY; ENVIRONMENTAL TEMPERATURE; FORECASTING; GENETIC ALGORITHM; HUMIDITY; INTERMETHOD COMPARISON; LEARNING; MARKETING; MATHEMATICAL COMPUTING; MONTE CARLO METHOD; NONLINEAR SYSTEM; NORMAL DISTRIBUTION; PARSIMONY ANALYSIS; PREDICTION; PRIORITY JOURNAL; PROBABILITY; REPRODUCIBILITY; UNITED STATES; VALIDATION PROCESS; VELOCITY; WEATHER; WIND;

EID: 33750297514     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2005.12.131     Document Type: Article

References (34)

1. Baeck T. Evolutionary Algorithm in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms (1995), Oxford University Press, New York
2. Baeck T., Hammel U., and Schwefel H.-P. Evolutionary computation: comments on the history and current state. IEEE Trans. Evol. Comput. 1 (1997) 3-17
3. Baeck T., Fogel D.B., and Michalewicz Z. Evolutionary Computation I. Basic Algorithm and Operators vol. 1 (2000), Institute of Physics Publishing, Bristol
4. Baeck T., Fogel D.B., and Michalewicz Z. Evolutionary Computataion II. Advanced Algorithm and Operators 2 (2000), Institute of Physics Pub., Bristol
5. Bishop C. Neural Networks for Pattern Recognition (1995), Oxford University Press Inc.
6. Bunn D.W. Forecasting loads and prices in competitive power markets. Proc. IEEE 88 (2000) 163-169
7. Bunn D.W., and Farmer E.D. Economic and operational context of electric load prediction. In: Bunn D.W., and Farmer E.D. (Eds). Comparative Models for Electrical Load Forecasting (1985), Wiley, Newyork 3-12
8. Z.S.H. Chan, H.W. Ngan, A.B. Rad, T.K. Ho, Alleviating "overfitting" via genetically-regularised neural network, Electronics Letter, 2002.
9. Charytoniuk W., and Chen M.-S. Very short-term load forecasting using artificial neural networks. IEEE Trans. Power Syst. 15 (2000) 263-268
10. Chen S. Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Trans. Neural Networks 10 (1999) 1239-1243
11. Doveh E., Feigin P., Greig D., and Hyams L. Experience with FNN models for medium term power demand predictions. IEEE Trans. Power Syst. 14 (1999) 538-546
12. Gamerman D. Markov Chain Monte-Carlo:Stochastic simulation for Bayesian inference (1997), Chapman & Hall, London
13. Gilks W.R., Richardson S., and Spiegelhalter D.J. Markov Chain Monte-Carlo in Practice (1996), Chapman & Hall, London
14. Grady W.M., Groce L.A., Huebner T.M., Lu Q.C., and Crawford M.M. Enhancement, implementation and performance of an adaptive short-term load forecasting algorithm. IEEE Trans. Power Systems 6 (1991) 1404-1410
15. Hippert H.S., Pedreira C.E., and Souza R.C. Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. Power Systems 16 (2001) 44-55
16. Holland J.H. Adaptation in Natural and Artificial Systems (1975), The University of Michigan Press, Ann Arbor, MI
17. Khotanzad A., Afkhami-Rohani R., Tsun-Liang L., Abaye A., Davis M., and Maratukulam D.J. ANNSTLF-a neural-network-based electric load forecasting system. IEEE Trans. Neural Networks 84 (1997) 835-846
18. Khotanzad A., Afkhami-Rohani R., and Maratukulam D.J. ANNSTLF-artificial neural network short-term load forecaster-generation three. IEEE Trans. Power Systems 134 (1998) 1413-1422
19. Kotz S., Balakrishnan N., and Johnson N.L. Continuous Multivariate Distributions. second ed (2000), Wiley, Newyork
20. Koza J.R. Genetic Programming (1992), MIT Press, Cambridge, MA
21. Larsen J., Svarer C., Andersen L.N., and Hansen L.K. Adaptive regularization in neural network modeling. In: Orr G.B., and Muller K.-R. (Eds). Neural Networks: Tricks of the Trade (1998), Springer, Berlin 113-132
22. Lu Q.C., Grady W.M., and Crawford M.M. An adaptive algorithm for short-term multinode load forecasting in power systems. IEEE Trans. Circuits Syst. 35 (1988) 1004-1010
23. D.J.C. MacKay, A practical Bayesian framework for backprop networks, in: J.E. Moody, S.J. Hanson, R.P. Lippmann, (Eds.), Adv. Neural Inform. Process. Syst., 4 (1992) 839-846.
24. MacKay D.J.C. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks. Comput Neural Syst. 6 (1995) 469-505
25. D.J.C. MacKay, Bayesian methods for neural networks - FAQ, vol. 2001, 2000.
26. MacKay D.J.C. Bayesian Methods for Neural Networks: Theory and Applications (2001), Cavendish Laboratory, Cambridge Neural Networks Summer School lecture notes
27. R.M. Neal, Bayesian Training of Backpropagation Networks by the Hybrid Monte-Carlo Method, Connectionist Research Group, Department of Computer Science, University of Toronto CRG-TR-92-1, 10 April 1992.
28. R.M. Neal, Probabilistic Inference Using Markov Chain Monte-Carlo Methods, Department of Computer Science, University of Toronto CRG-TR-93-1, 25 September 1993.
29. R.M. Neal, Bayesian Learning for Neural Networks; Springer, New York, 1996.
30. Rognvaldsson T. A Simple Trick for Estimating the Weight Decay Parameter. In: Orr G.B., and Muller K.-R. (Eds). Neural Networks: Tricks of the Trade (1998), Springer, Berlin 71-92
31. Schneider A.M., Takenawa T., and Schiffman D.A. 24-hour electric utility load forecasting. In: Bunn D.W., and Farmer E.D. (Eds). Comparative Models for Electrical Load Forecasting (1985), Wiley, New York 87-108
32. K. Siwek, S. Osowski, Regularization of neural networks for improved load forecasting in power system, presented at ICECS 2001, 2001.
33. E.H. Tito, G. Zaverucha, M. Vellasco, M. Pacheco, Bayesian neural networks for electric load forecasting, presented at International Conference on Neural Information Processing, 1999.
34. Yao X., and Liu Y. A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Networks 8 (1997) 694-713