Short-term ANN load forecasting from limited data using generalization learning strategies
The emergence of the new competitive electricity market environment has made short-term load forecasting a more complex task, owing to the effect of marketers’ behavior on the load pattern and the reduction of available information due to commercial reasons. In recent years, many ANN-based forecaste...
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| Published in: | Neurocomputing (Amsterdam) Vol. 70; no. 1; pp. 409 - 419 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01.12.2006
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | The emergence of the new competitive electricity market environment has made short-term load forecasting a more complex task, owing to the effect of marketers’ behavior on the load pattern and the reduction of available information due to commercial reasons. In recent years, many ANN-based forecasters are proposed for learning the highly nonlinear load pattern, yet their effectiveness are limited by the reduction of training data, which causes these ANN models to be susceptible to “over-fitting”. “Over-fitting” is a common ANN problem that describes the situation that the model memorizes the training data but fails to generalize well to new data.
This paper discusses the problem of “over-fitting” and some common generalization learning techniques in the ANN literature, as well as introducing a new Genetic Algorithm-based regularization method called “GARNET” for short-term load forecasting. As an illustration, four generalization learning techniques, including Early-Stopping, Bayesian Regularization, Adaptive-Regularization and GARNET are applied to train Multi-Layer Perceptrons networks (MLP) for day-ahead load forecasting on limited amount of hourly data from a US utility. Results show that forecasters trained by these four methods consistently produce lower prediction error than those trained by the standard error minimization method. |
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| AbstractList | The emergence of the new competitive electricity market environment has made short-term load forecasting a more complex task, owing to the effect of marketers’ behavior on the load pattern and the reduction of available information due to commercial reasons. In recent years, many ANN-based forecasters are proposed for learning the highly nonlinear load pattern, yet their effectiveness are limited by the reduction of training data, which causes these ANN models to be susceptible to “over-fitting”. “Over-fitting” is a common ANN problem that describes the situation that the model memorizes the training data but fails to generalize well to new data.
This paper discusses the problem of “over-fitting” and some common generalization learning techniques in the ANN literature, as well as introducing a new Genetic Algorithm-based regularization method called “GARNET” for short-term load forecasting. As an illustration, four generalization learning techniques, including Early-Stopping, Bayesian Regularization, Adaptive-Regularization and GARNET are applied to train Multi-Layer Perceptrons networks (MLP) for day-ahead load forecasting on limited amount of hourly data from a US utility. Results show that forecasters trained by these four methods consistently produce lower prediction error than those trained by the standard error minimization method. |
| Author | Rad, A.B. Ngan, H.W. Kasabov, N. Chan, Zeke S.H. David, A.K. |
| Author_xml | – sequence: 1 givenname: Zeke S.H. surname: Chan fullname: Chan, Zeke S.H. email: zekechan@vodafone.net.nz organization: Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand – sequence: 2 givenname: H.W. surname: Ngan fullname: Ngan, H.W. organization: Department of Electrical Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong – sequence: 3 givenname: A.B. surname: Rad fullname: Rad, A.B. organization: Department of Electrical Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong – sequence: 4 givenname: A.K. surname: David fullname: David, A.K. organization: Department of Electrical Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong – sequence: 5 givenname: N. surname: Kasabov fullname: Kasabov, N. organization: Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand |
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| Keywords | Day-ahead forecasting Genetic algorithm Regularization Open electricity market |
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| References | Chen (bib10) 1999; 10 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. Baeck, Hammel, Schwefel (bib2) 1997; 1 Charytoniuk, Chen (bib9) 2000; 15 Rognvaldsson (bib30) 1998 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. Baeck (bib1) 1995 MacKay (bib24) 1995; 6 K. Siwek, S. Osowski, Regularization of neural networks for improved load forecasting in power system, presented at ICECS 2001, 2001. Larsen, Svarer, Andersen, Hansen (bib21) 1998 R.M. Neal, Bayesian Learning for Neural Networks; Springer, New York, 1996. Baeck, Fogel, Michalewicz (bib4) 2000; 2 Gamerman (bib12) 1997 Kotz, Balakrishnan, Johnson (bib19) 2000 Hippert, Pedreira, Souza (bib15) 2001; 16 MacKay (bib26) 2001 Lu, Grady, Crawford (bib22) 1988; 35 Holland (bib16) 1975 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. Grady, Groce, Huebner, Lu, Crawford (bib14) 1991; 6 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. Bishop (bib5) 1995 Z.S.H. Chan, H.W. Ngan, A.B. Rad, T.K. Ho, Alleviating “overfitting” via genetically-regularised neural network, Electronics Letter, 2002. Yao, Liu (bib34) 1997; 8 Koza (bib20) 1992 D.J.C. MacKay, Bayesian methods for neural networks - FAQ, vol. 2001, 2000. Schneider, Takenawa, Schiffman (bib31) 1985 Baeck, Fogel, Michalewicz (bib3) 2000; vol. 1 Doveh, Feigin, Greig, Hyams (bib11) 1999; 14 Khotanzad, Afkhami-Rohani, Tsun-Liang, Abaye, Davis, Maratukulam (bib17) 1997; 84 Khotanzad, Afkhami-Rohani, Maratukulam (bib18) 1998; 134 Gilks, Richardson, Spiegelhalter (bib13) 1996 Bunn (bib6) 2000; 88 Bunn, Farmer (bib7) 1985 Schneider (10.1016/j.neucom.2005.12.131_bib31) 1985 Baeck (10.1016/j.neucom.2005.12.131_bib4) 2000; 2 Khotanzad (10.1016/j.neucom.2005.12.131_bib17) 1997; 84 Larsen (10.1016/j.neucom.2005.12.131_bib21) 1998 10.1016/j.neucom.2005.12.131_bib32 10.1016/j.neucom.2005.12.131_bib33 Khotanzad (10.1016/j.neucom.2005.12.131_bib18) 1998; 134 MacKay (10.1016/j.neucom.2005.12.131_bib24) 1995; 6 Baeck (10.1016/j.neucom.2005.12.131_bib3) 2000; vol. 1 Bunn (10.1016/j.neucom.2005.12.131_bib6) 2000; 88 Holland (10.1016/j.neucom.2005.12.131_bib16) 1975 Gamerman (10.1016/j.neucom.2005.12.131_bib12) 1997 10.1016/j.neucom.2005.12.131_bib25 10.1016/j.neucom.2005.12.131_bib27 10.1016/j.neucom.2005.12.131_bib28 10.1016/j.neucom.2005.12.131_bib29 Hippert (10.1016/j.neucom.2005.12.131_bib15) 2001; 16 10.1016/j.neucom.2005.12.131_bib8 Gilks (10.1016/j.neucom.2005.12.131_bib13) 1996 Grady (10.1016/j.neucom.2005.12.131_bib14) 1991; 6 Koza (10.1016/j.neucom.2005.12.131_bib20) 1992 10.1016/j.neucom.2005.12.131_bib23 Yao (10.1016/j.neucom.2005.12.131_bib34) 1997; 8 Charytoniuk (10.1016/j.neucom.2005.12.131_bib9) 2000; 15 Doveh (10.1016/j.neucom.2005.12.131_bib11) 1999; 14 Baeck (10.1016/j.neucom.2005.12.131_bib2) 1997; 1 Rognvaldsson (10.1016/j.neucom.2005.12.131_bib30) 1998 Bunn (10.1016/j.neucom.2005.12.131_bib7) 1985 Lu (10.1016/j.neucom.2005.12.131_bib22) 1988; 35 MacKay (10.1016/j.neucom.2005.12.131_bib26) 2001 Kotz (10.1016/j.neucom.2005.12.131_bib19) 2000 Baeck (10.1016/j.neucom.2005.12.131_bib1) 1995 Bishop (10.1016/j.neucom.2005.12.131_bib5) 1995 Chen (10.1016/j.neucom.2005.12.131_bib10) 1999; 10 |
| References_xml | – reference: 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. – reference: K. Siwek, S. Osowski, Regularization of neural networks for improved load forecasting in power system, presented at ICECS 2001, 2001. – reference: 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. – year: 1992 ident: bib20 article-title: Genetic Programming – year: 2001 ident: bib26 article-title: Bayesian Methods for Neural Networks: Theory and Applications – reference: 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. – year: 1995 ident: bib5 article-title: Neural Networks for Pattern Recognition – volume: 15 start-page: 263 year: 2000 end-page: 268 ident: bib9 article-title: Very short-term load forecasting using artificial neural networks publication-title: IEEE Trans. Power Syst. – year: 1996 ident: bib13 article-title: Markov Chain Monte-Carlo in Practice – volume: 14 start-page: 538 year: 1999 end-page: 546 ident: bib11 article-title: Experience with FNN models for medium term power demand predictions publication-title: IEEE Trans. Power Syst. – volume: 84 start-page: 835 year: 1997 end-page: 846 ident: bib17 article-title: ANNSTLF-a neural-network-based electric load forecasting system publication-title: IEEE Trans. Neural Networks – start-page: 87 year: 1985 end-page: 108 ident: bib31 article-title: 24-hour electric utility load forecasting publication-title: Comparative Models for Electrical Load Forecasting – volume: 35 start-page: 1004 year: 1988 end-page: 1010 ident: bib22 article-title: An adaptive algorithm for short-term multinode load forecasting in power systems publication-title: IEEE Trans. Circuits Syst. – start-page: 71 year: 1998 end-page: 92 ident: bib30 article-title: A Simple Trick for Estimating the Weight Decay Parameter publication-title: Neural Networks: Tricks of the Trade – volume: 1 start-page: 3 year: 1997 end-page: 17 ident: bib2 article-title: Evolutionary computation: comments on the history and current state publication-title: IEEE Trans. Evol. Comput. – reference: D.J.C. MacKay, Bayesian methods for neural networks - FAQ, vol. 2001, 2000. – reference: Z.S.H. Chan, H.W. Ngan, A.B. Rad, T.K. Ho, Alleviating “overfitting” via genetically-regularised neural network, Electronics Letter, 2002. – volume: 10 start-page: 1239 year: 1999 end-page: 1243 ident: bib10 article-title: Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks publication-title: IEEE Trans. Neural Networks – year: 1995 ident: bib1 publication-title: Evolutionary Algorithm in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms – start-page: 3 year: 1985 end-page: 12 ident: bib7 article-title: Economic and operational context of electric load prediction publication-title: Comparative Models for Electrical Load Forecasting – volume: 6 start-page: 469 year: 1995 end-page: 505 ident: bib24 article-title: Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks publication-title: Comput Neural Syst. – volume: 8 start-page: 694 year: 1997 end-page: 713 ident: bib34 article-title: A new evolutionary system for evolving artificial neural networks publication-title: IEEE Trans. Neural Networks – volume: 2 year: 2000 ident: bib4 publication-title: Evolutionary Computataion II. Advanced Algorithm and Operators – start-page: 113 year: 1998 end-page: 132 ident: bib21 article-title: Adaptive regularization in neural network modeling publication-title: Neural Networks: Tricks of the Trade – volume: 134 start-page: 1413 year: 1998 end-page: 1422 ident: bib18 article-title: ANNSTLF-artificial neural network short-term load forecaster-generation three publication-title: IEEE Trans. Power Systems – year: 1997 ident: bib12 article-title: Markov Chain Monte-Carlo:Stochastic simulation for Bayesian inference – volume: 16 start-page: 44 year: 2001 end-page: 55 ident: bib15 article-title: Neural networks for short-term load forecasting: a review and evaluation publication-title: IEEE Trans. Power Systems – volume: 88 start-page: 163 year: 2000 end-page: 169 ident: bib6 article-title: Forecasting loads and prices in competitive power markets publication-title: Proc. IEEE – year: 2000 ident: bib19 article-title: Continuous Multivariate Distributions – volume: vol. 1 year: 2000 ident: bib3 publication-title: Evolutionary Computation I. Basic Algorithm and Operators – reference: R.M. Neal, Bayesian Learning for Neural Networks; Springer, New York, 1996. – reference: 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. – year: 1975 ident: bib16 article-title: Adaptation in Natural and Artificial Systems – volume: 6 start-page: 1404 year: 1991 end-page: 1410 ident: bib14 article-title: Enhancement, implementation and performance of an adaptive short-term load forecasting algorithm publication-title: IEEE Trans. Power Systems – volume: 15 start-page: 263 year: 2000 ident: 10.1016/j.neucom.2005.12.131_bib9 article-title: Very short-term load forecasting using artificial neural networks publication-title: IEEE Trans. Power Syst. doi: 10.1109/59.852131 – year: 1997 ident: 10.1016/j.neucom.2005.12.131_bib12 – volume: 6 start-page: 1404 year: 1991 ident: 10.1016/j.neucom.2005.12.131_bib14 article-title: Enhancement, implementation and performance of an adaptive short-term load forecasting algorithm publication-title: IEEE Trans. Power Systems doi: 10.1109/59.116982 – volume: 35 start-page: 1004 year: 1988 ident: 10.1016/j.neucom.2005.12.131_bib22 article-title: An adaptive algorithm for short-term multinode load forecasting in power systems publication-title: IEEE Trans. Circuits Syst. doi: 10.1109/31.1846 – year: 1992 ident: 10.1016/j.neucom.2005.12.131_bib20 – year: 1995 ident: 10.1016/j.neucom.2005.12.131_bib1 – start-page: 3 year: 1985 ident: 10.1016/j.neucom.2005.12.131_bib7 article-title: Economic and operational context of electric load prediction – ident: 10.1016/j.neucom.2005.12.131_bib25 – ident: 10.1016/j.neucom.2005.12.131_bib23 – volume: 6 start-page: 469 year: 1995 ident: 10.1016/j.neucom.2005.12.131_bib24 article-title: Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks publication-title: Comput Neural Syst. doi: 10.1088/0954-898X/6/3/011 – year: 2001 ident: 10.1016/j.neucom.2005.12.131_bib26 – ident: 10.1016/j.neucom.2005.12.131_bib27 – ident: 10.1016/j.neucom.2005.12.131_bib29 doi: 10.1007/978-1-4612-0745-0 – start-page: 71 year: 1998 ident: 10.1016/j.neucom.2005.12.131_bib30 article-title: A Simple Trick for Estimating the Weight Decay Parameter – volume: vol. 1 year: 2000 ident: 10.1016/j.neucom.2005.12.131_bib3 – ident: 10.1016/j.neucom.2005.12.131_bib32 – volume: 84 start-page: 835 year: 1997 ident: 10.1016/j.neucom.2005.12.131_bib17 article-title: ANNSTLF-a neural-network-based electric load forecasting system publication-title: IEEE Trans. Neural Networks doi: 10.1109/72.595881 – volume: 10 start-page: 1239 year: 1999 ident: 10.1016/j.neucom.2005.12.131_bib10 article-title: Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks publication-title: IEEE Trans. Neural Networks doi: 10.1109/72.788663 – volume: 16 start-page: 44 year: 2001 ident: 10.1016/j.neucom.2005.12.131_bib15 article-title: Neural networks for short-term load forecasting: a review and evaluation publication-title: IEEE Trans. Power Systems doi: 10.1109/59.910780 – year: 2000 ident: 10.1016/j.neucom.2005.12.131_bib19 – year: 1996 ident: 10.1016/j.neucom.2005.12.131_bib13 – volume: 88 start-page: 163 year: 2000 ident: 10.1016/j.neucom.2005.12.131_bib6 article-title: Forecasting loads and prices in competitive power markets publication-title: Proc. IEEE doi: 10.1109/5.823996 – ident: 10.1016/j.neucom.2005.12.131_bib33 – volume: 2 year: 2000 ident: 10.1016/j.neucom.2005.12.131_bib4 – year: 1975 ident: 10.1016/j.neucom.2005.12.131_bib16 – volume: 1 start-page: 3 year: 1997 ident: 10.1016/j.neucom.2005.12.131_bib2 article-title: Evolutionary computation: comments on the history and current state publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.585888 – start-page: 113 year: 1998 ident: 10.1016/j.neucom.2005.12.131_bib21 article-title: Adaptive regularization in neural network modeling – ident: 10.1016/j.neucom.2005.12.131_bib8 doi: 10.1049/el:20020592 – volume: 8 start-page: 694 year: 1997 ident: 10.1016/j.neucom.2005.12.131_bib34 article-title: A new evolutionary system for evolving artificial neural networks publication-title: IEEE Trans. Neural Networks doi: 10.1109/72.572107 – volume: 14 start-page: 538 year: 1999 ident: 10.1016/j.neucom.2005.12.131_bib11 article-title: Experience with FNN models for medium term power demand predictions publication-title: IEEE Trans. Power Syst. doi: 10.1109/59.761878 – volume: 134 start-page: 1413 year: 1998 ident: 10.1016/j.neucom.2005.12.131_bib18 article-title: ANNSTLF-artificial neural network short-term load forecaster-generation three publication-title: IEEE Trans. Power Systems doi: 10.1109/59.736285 – ident: 10.1016/j.neucom.2005.12.131_bib28 – start-page: 87 year: 1985 ident: 10.1016/j.neucom.2005.12.131_bib31 article-title: 24-hour electric utility load forecasting – year: 1995 ident: 10.1016/j.neucom.2005.12.131_bib5 |
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| Title | Short-term ANN load forecasting from limited data using generalization learning strategies |
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