Abstract
This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained from the Czech Electric Power Utility for the last seven years (January 1994 — December 2000). A comparison of the proposed techniques is presented for predicting 48 hourly (2 day ahead) demands for electricity. Simulation results indicate that hybrid fuzzy neural network and radial basis function networks are the best candidates for the analysis and forecasting of electricity demand.
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Khan, M.R., Abraham, A., Ondrůšek, Č. (2002). Soft Computing for Developing Short Term Load Forecasting Models in Czech Republic. In: Abraham, A., Köppen, M. (eds) Hybrid Information Systems. Advances in Soft Computing, vol 14. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1782-9_16
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DOI: https://doi.org/10.1007/978-3-7908-1782-9_16
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1480-4
Online ISBN: 978-3-7908-1782-9
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