By Carpinelli G., Testa A.
Certain factor: chosen papers from PMAPS 2002 -Conference on Probabilistic tools utilized to strength structures, Naples 2002
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Additional resources for Compel (Vol. 23, 2004): Special Issue
And Stahlkopf, K. (2001), “California syndrome”, Power Economics – EPRI Journal, pp. 24-7. Ha¨rdle, W. (1991), Smoothing Techniques, Springer-Verlag, Berlin. S. and Papadopoulos, M. (1990), “Probabilistic calculations of aggregate storage heating loads”, IEEE Trans. Power Delivery, Vol. 5 No. 3, pp. 1520-6. Ihara, S. C. (1981), “Physically based modeling of cold load pickup”, IEEE Trans. Power Apparatus Systems, Vol. 100 No. 9, pp. 4142-50. C. P. (1994), “A physically-based computer model of aggregate electric water heating loads”, IEEE Trans.
Discrete state. 5-18C). The assumption that the loads share the same values of model parameters, for example, thermal resistors or capacities, does not hold in real life situations. The differences between the parameters in SSE for different similar dwellings are due to: different constructive characteristics (room surface, type of window, glass surface) which influence R and C parameters; different heating device characteristics: energy efficient ratio (EER) values and different power consumption (fluctuation on voltage levels) that affects the flux transferred to the indoor environment.
An approximated optimal bandwidth is then used, which, for the Gaussian kernel is (Ha¨rdle, 1991), ! ^ R N ÿ1=5 ; ð14Þ h^ 0 ¼ 1:06 min s^; 1:34 where s^ and R^ are the standard deviation and the inter-quartile range calculated from the data. Figure 6 shows the block diagram of the global Application of smoothing techniques 57 COMPEL 23,1 58 Figure 6. , 1999), which is being considered as an international standard (ISO) for information technology. Matlab software tool has been used to develop the scripts.