Abstract
The main topic in this work was the development of a hybrid intelligent system for the hourly load forecasting in a time period of 7 days ahead, using a combination of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System. The hourly load forecasting was accomplished in two steps: in the first one, two ANNs are used to forecast the total load of the day, where one of the networks forecasts the working days (Monday through Friday), and the other forecasts the Saturdays, Sundays and public holidays; in the second step, the ANFIS was used to give the hourly consumption rate of the load. The proposed system presented a better performance as against the system currently used by Energy Company of Pernambuco, named PREVER. The simulation results showed an hourly mean absolute percentage error of 2.81% for the year 2005.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agência Nacional de Energia Elétrica – Procedimentos de Distribuição de Energia Elétrica no Sistema Elétrico Nacional – PRODIST Módulo 4.W. In: Portuguese
Operador Nacional do Sistema Elétrico - Consolidação da Previsão de Carga, Procedimentos de Rede, Submódulo 5.1. In Portuguese
Montgomery, D.C., Johnson, L.A., Gardiner, J.S.: Forecasting and Time Series Analysis. In: McGraw-Hill International Editions (1990)
Bakirtzis, A., Petrldis, V.S., Kiartiz, 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., Lu, T., Abaye, A., Davis, M., Maratukulam, D.J.: A Neural-Network-Based Electric Load Forecasting System. IEEE Transactions on Neural networks, 8(4), 835–845 (1997)
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)
Srinivasan, D., Tan, S.S., Chang, C.S., et al.: Parallel Neural Network-Fuzzy Expert System Strategy For Short-term Load Forecasting: System Implementation And Performance Evaluation. IEEE Transactions on Power Systems, 14(3) (1999)
Aquino, R.R.B., Ferreira, A.A., Lira, M.M.S., Carvalho Jr., M.A., Nóbrega Neto, O., Silva, G.B.: Combining artificial neural networks and heuristic rules in a hybrid intelligent load forecast system. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4132, pp. 757–766. Springer, Heidelberg (2006)
Ferreira, A.A.: Comparação de arquiteturas de redes neurais para sistemas de reconhecimento de padrões em narizes artificiais. In: Dissertação de mestrado. UFPE, Recife-PE, In Portuguese (2004)
Jang, J.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. Electric Power Systems Research / IEEE Transactions on systems 23(3), 169–176 (1993)
Chiu, S.: Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent & Fuzzy Systems 2(3) (1994)
Kim, K.H., Youn, H.S., Kang, Y.C., et al.: Short-Term Load Forecasting for Special Days in Anomalous Load Conditions Using Neural Networks. IEEE Transactions on Power Systems 15(2) (1993)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Aquino, R.R.B. et al. (2007). Combined Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System for Improving a Short-Term Electric Load Forecasting. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_80
Download citation
DOI: https://doi.org/10.1007/978-3-540-74695-9_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74693-5
Online ISBN: 978-3-540-74695-9
eBook Packages: Computer ScienceComputer Science (R0)