Computational Problems in Science and Engineering by Nikos Mastorakis, Aida Bulucea, George Tsekouras

By Nikos Mastorakis, Aida Bulucea, George Tsekouras

This ebook offers readers with smooth computational options for fixing number of difficulties from electric, mechanical, civil and chemical engineering. Mathematical equipment are provided in a unified demeanour, to allow them to be utilized regularly to difficulties in utilized electromagnetics, power of fabrics, fluid mechanics, warmth and mass move, environmental engineering, biomedical engineering, sign processing, computerized keep watch over and more.

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Optimal smoothing for trend removal in short term electricity demand forecasting. IEEE Trans. Power Syst. 13(3), 1115–1120 (1998) 12. : Nonparametric regression based short-term load forecasting. IEEE Trans. Power Syst. 13(3), 725–730 (1998) 13. : An adaptive modular artificial neural network hourly load forecaster and its implementation at electric utilities. IEEE Trans. Power Syst. 10(3), 1716–1722 (1995) 14. : ANNSTLF- neural network-based electric load forecasting system. IEEE Trans. Neural Netw.

The use of validation test set is necessary as it helps to achieve better generalization results and it seems to have similar behaviour with the test set rather than the training set. Fig. 11 MAPE of training, validation and test sets versus the training years. J. Tsekouras et al. Fig. 12 MAPE of training, validation and test sets versus the percentage of the data used for ANN evaluation. Scaled conjugate gradient training algorithm is used Another important issue that should be carefully studied is the percentage of the data used for the evaluation of the model.

In Fig. 9 the prediction errors of a typical summer day for Greek interconnected power system of the year 2000 (Thursday 8-6-2000) are presented for the training, evaluation and test sets respectively, while in Fig. J. Tsekouras et al. Fig. 10 Chronological active load curves of the measured load, the estimated load, the estimated load with the 5 % lower limit with respect to evaluation set, the estimated load with the 5 % upper limit with respect to evaluation set, the estimated load with the 5 % lower limit with respect to test set, the estimated load with the 5 % upper limit with respect to test set for the best ANN model for 8-6-2000 in Greek interconnected power system the respective measured and estimated load values are presented together with the 90 % confidence intervals of the evaluation and the test data sets.

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