Abstract
In this work, an Artificial Neural Network (ANN) is combined to Heuristic Rules producing a powerful hybrid intelligent system for short and mid-term electric load forecasting. The Heuristic Rules are used to adjust the ANN output to improve the system performance. The study was based on load demand data of Energy Company of Pernambuco (CELPE), which contain the hourly load consumption in the period from January-2000 until December-2004. The more critical period of the rationing in Brazil was eliminated from the data file, as well as the consumption of the holidays. For this reason, the proposed system forecasts a holiday as one Saturday or Sunday based on the specialist’s information. The result obtained with the proposed system is compared with the currently system used by CELPE to test its effectiveness. In addition, it was also compared to the result of the ANN acting alone.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Montgomery, D.C., Johnson, L.A., Gardiner, J.S.: Forecasting and Time Series Analysis. McGraw-Hill International Editions, New York (1990)
Bakirtzis, A., Petrldis, V., Kiartiz, S.J., Alexiardis, M.C.: A Neural Network Short Term Load Forecasting Model for the Greek Power System. IEEE Transactions on Power Systems 11(2), 858–863 (1996)
Khotanzad, A., Afkhami-rohani, R., Maratukulam, D.J.: ANNSTLF - Artificial Neural Network Short-Term Load Forecaster-Generation Three. IEEE Transactions on Power Systems 13(4), 1413–1422 (1998)
Kim, C., Yu, I., Song, Y.H.: Kohonen Neural Network and Wavelet Transform Based Approach to Short-Term Load Forecasting. Electric Power Systems Research 63(3), 169–176 (2002)
Papadakis, S.E., Theocharis, J.B., Kiartzis, S.J., Bakirtzis, A.G.: A novel approach to Short-term Load Forecasting using Fuzzy neural networks. IEEE Transactions on Power Systems 13(2), 480–492 (1998)
Silva, A., Moulin, L., Reis, A.J.R.: Feature Extraction via Multiresolution Analysis for Short-Term Load Forecasting. IEEE Transactions on Power Systems 20(1), 189–198 (2005)
Aquino, R.R.B., Ferreira, A.A., Silva, G.B., Nóbrega Neto, O., Lira, M.M.S., Oliveira, J.B.: Previsão de Carga Horária em Médio Prazo Utilizando Redes Neurais com Foco na Previsão dos Feriados. VII CBRN – Congresso Brasileiro de Redes Neurais, Natal (2005) ISSN: 1808-8589
Prechelt, L.: Proben1 – A Set of Neural Network Benchmark Problems and Benchmarking Rules. Technical Report, pp. 21-94 (1994)
Ferreira, A.A.: Comparação de arquiteturas de redes neurais para sistemas de reconhecimento de padrões em narizes artificiais. Master dissertation, Center of Informatics, Federal University of Pernambuco, Recife (2004)
Witten, W., Frank, E.: Data Mining, Practical Machine Learning Tools and Techniques with Java Implementations, p. 126. Morgan Kaufmann Publishers, San Francisco (2000)
Riedmiller, M., Braun, H.: A Direct adaptive Method for faster Backpropagation Learning: The RPROP Algorithm. IEEE International Conference on Neural Networks 1, 586–591 (1993)
Gonzales, A.J., Dankel, D.D.: The Engineering of Knowledge-Based Systems. Prentice Hall, Englewood Cliffs (1993)
Aquino, R.R.B., Ferreira, A.A., Medeiros, L.H., Silva, G.B., Nóbrega Neto, O.: Previsão de Carga em Curto e Médio Prazo Utilizando Redes Neurais Artificiais: Uma Aplicação ao Sistema CELPE. SENDI - XVI Seminário Nacional de Distribuição de Energia Elétrica, Brasília (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Aquino, R.R.B., Ferreira, A.A., Carvalho, M.A., Lira, M.M.S., Silva, G.B., Neto, O.N. (2006). Combining Artificial Neural Networks and Heuristic Rules in a Hybrid Intelligent Load Forecast System. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_79
Download citation
DOI: https://doi.org/10.1007/11840930_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
eBook Packages: Computer ScienceComputer Science (R0)