In this paper, a solar cooling installation is analyzed with the aim of optimizing its performances. The system consists of vacuum solar collectors, which supply hot water to a LiBr absorption chiller. A boiler can be used to supply an additional amount of hot water in the case of insufficient solar radiation. In addition, a vapor compression chiller operates as a backup system and integrates the solar driven system in the case of large cooling request. Such system gives multiple operating options, especially at partial load. A model of the system is presented and applied to the real plant. It is shown that if a multi-objective optimization is performed, considering minimum primary energy consumption from fossil fuel and maximum utilization of the absorption system, a Pareto front is obtained. This occurs because the two objective functions are competing. A control strategy based on the use of neural networks is presented. Input variables are the solar radiation, ambient temperature and the cooling request. In this work the control strategy is adjusted in order to reach the minimum fossil energy consumption, but the same approach can be applied with other objective functions.

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