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In the present study, a method for sizing remote PV systems based on genetic algorithms has been compared with two classical methods, worst month method and loss of power supply probability (LPSP) method.
In the present study, a method for sizing remote PV systems based on GA [19] has been compared with two classical methods, worst month method [1] and LPSP method [20].
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These results show that very simple method, like worst case method, can give good results under particular conditions, but the problem is to determine the adequate autonomy duration to obtain the lowest cost with non-unmet load.
However, simple methods like worst month method are still largely used in sizing simple PV systems.
A closer look on the obtained enrichment factors for each target reveals that all methods performed worst against the CP3A4 and CP2C9 targets (both cytochrome P450s).
While the distance measure is 'Squared Euclidean', the Nearest clustering method is the worst method of all.
Again, the proposed method generated the best results while the intensity-based optimization method performs worst.
Despite avoiding the use of toxic reagents, the nitrite iodide method presented worst performance in terms of selectivity and sensitivity.
For topologies with different design methods, the worst-case connectivity ratio is used to verify the effectiveness of the combination method.
Performance characteristics of cooling modules are obtained from tests under steady-state conditions, and analysis methods assume worst-case heat loads, which leads to cooling package over-design.
In our second method, the worst QoS in the set is maximized subject to the constraints of individual relay transmit powers.
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