Abstract
This work uses artificial neural networks, whose architecture were developed using genetic algorithm to realize the hourly load forecasting based on the monthly total load consumption registered by the Energy Company of Pernambuco (CELPE). The proposed Hybrid Intelligent System – HIS was able to find the trade-off between forecast errors and network complexity. The load forecasting produces the essence to increase and strengthen in the basic grid, moreover study into program and planning of the system operation. The load forecasting quality contributes substantially to indicating more accurate consuming market, and making electrical system planning and operating more efficient. The forecast models developed comprise the period of 45 and 49 days ahead. Comparisons between the four models were achieved by using historical data from 2005.
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References
Montgomery, D.C., Johnson, L.A., Gardner, J.S.: Forecasting and time series analysis. Ed. 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)
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)
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)
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)
Khotanzad, A., Davis, M.H., Abaye, A., Maratukulam, D.J.: An artificial neural network hourly temperature forecaster with applications in load forecasting. IEEE Transactions on Power Systems 11(2), 870–876 (1996)
Drinivasan, D., Tan, S.S., Chang, C.S.: 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, J.M.A., Neto, N.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)
Neto, N.O.: Aplicação de redes neurais artificiais e algoritmos genéticos na previsão de carga elétrica em médio prazo. In: Dissertação (Mestrado em Engenharia Elétrica – CTG, UFPE. Recife-PE, In Portuguese (2006)
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de Aquino, R.R.B., Neto, O.N., Lira, M.M.S., Ferreira, A.A., Santos, K.F. (2007). Using Genetic Algorithm to Develop a Neural-Network-Based 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_76
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DOI: https://doi.org/10.1007/978-3-540-74695-9_76
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