Energy Systems. Adaptive Complexity by T. Ohta (Auth.)

By T. Ohta (Auth.)

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In: Proc. of the IEEE Int. Conf. ICNN'95, vol. 5, Perth (Western Australia) 1995. p. 2314-19. A. KalogirouI Applied Energy 67 (2000) 17-35 35 Kajl S, Roberge MA, Lamarche L, Malinowski P. Evaluation of building energy consumption based on fuzzy logic and neural networks applications. In: Proc. , Brussels (Belgium) 1997. p. 264. Roberge MA, Lamarche L, Karjl S, Moreau A. Model of room storage heater and system identifi­ cation using neural networks. In: Proc. , Brussels (Belgium), 1997 p. 265. Negnevitsky M.

The K\ links of the A\ elements are connected to M2 through a new link of unit cost K2. Because the new link (perhaps, an advanced mode of transportation) facilitates the transport of j=l 1 l,opt j=2 l,opt M, M, Mi K 2( k 2, f 2) Mi Fig. 2. The second area A2, as a construct of n2 elemental areas; the double arrow means that the flow of goods may proceed in either direction. A. Bejan et al / Applied Energy 67 (2000) 37-60 43 a stream that is larger than in each elemental area, its unit cost is lower than in the elements, provided Ki

New, relative to the results of the cost minimization (Section 2), is the optimal size of the elemental area. Accord­ ing to Eq. e. large g) and the transport is inexpensive (small ko/ko and k\/f\). This optimal area size is independent of the surface density of the flow of goods γ because it is essentially a balance between the revenue generated by the stream of goods and the cost of transporting the same stream. The twice-maximized revenue per unit area is equal to Kg/3. Consider next the second-order area element Ai of Fig.

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