Abstract
This work presents the development of a novel hybrid system called Hierarchical Neuro-Fuzzy BSP (HNFB) and its application in electric load forecasting. The HNFB system is based on the BSP partitioning (Binary Space Partitioning) of the input space and has been developed in order to bypass the traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity to create their own structure. To test the HNFB system, we have used monthly load data of six electric energy companies. The results are compared with other forecast methods, such as Neural Networks and Box & Jenkins.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Halgamuge, S. K., Glesner, M.: Neural Networks in Designing Fuzzy Systems for Real World Applications. Fuzzy Sets and Systems N0.65, pp. 1–12. (1994).
Haykin, S.: Neural Networks-A Comprehensive Foudation. Macmillan College Publishing Company, Inc.(1994).
Kruse, R., Nauck, D.: NEFFCLASS-A Neuro-Fuzzy Approach for the Classification of Data. Proc. Of the 1995 ACM Symposium on Applied Computing, Nashville.
Mackay, D., “Bayesian Methods for Adaptive Models”, Ph.D. thesis, California Institute of Technology, 1992.
Mackay, D., A Practical Bayesian Framework for Backpropagation Networks. Neural Computation, 4 (3):448–472, 1992.
Mendel, J.: Fuzzy Logic Systems for Engineering: A Tutorial. Proceedings of the IEEE, Vol. 83, n.3, pp.345–377. (1995).
Neal, R., “Bayesian Learning for Neural Networks”, Lecture Notes in Statistics 118, Springer Verlag, 1996.
Souza, Flávio Joaquim de: Modelos Neuro-Fuzzy Hierárquicos. Tese de Doutorado. Departamento de Engenharia Elétrica da Pontifícia Universidade Católica do Rio de Janeiro (1999) (in portuguese).
Souza, F. J., Vellasco, M. M. B. R., Pacheco, M. A. C, Hierarchical Neuro-Fuzzy QuadTree Models, Fuzzy Sets & Systems, (to be published), 2001.
Tito, E. H., Zaverucha, G., Vellasco, M. M. B. R., Pacheco, M. A. C, “Applying Bayesian Neural Networks to Electrical Load Forecasting”, Proceedings of The 6th International Conference on Neural Information Processing (ICONIP’99), Perth, Australia, 16–20 November 1999.
Zebulum, R. S., Vellasco, M. M. B. R., Guedes, K., and Pacheco, M. A. C, An Intelligent Load Forecasting System, Proceedings of the IEEE International Conference on Electricity Sector Development and Demand Side Management-ESDDSM’95, pp. 96–103, Kuala Lumpur, Malasia, 21-22 November 1995.
Zebulum, R. S., Guedes, K., Vellasco, M. M. B. R., and Pacheco, M. A. C, Short-Term Load Forecasting Using Neural Nets, Lecture Notes in Computer Science 930, From Natural to Artificial Neural Computation, Springer-Verlag, Proceedings of the International Workshop on Artificial Neural Networks (IWANN’95), pp.1001–1008, Torremolinos (Málaga), Spain, 7-9 June 1995.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Souza, F.J., Vellasco, M.M.R., Pacheco, M.A.C. (2001). The Hierarchical Neuro-Fuzzy BSP Model: An Application in Electric Load Forecasting. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_21
Download citation
DOI: https://doi.org/10.1007/3-540-45720-8_21
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42235-8
Online ISBN: 978-3-540-45720-6
eBook Packages: Springer Book Archive