Thermo-economics analysis of energy-related systems is a combination of thermodynamic principles and its impact on economy. It primarily determines the difference between actual and expected performance based on exergy and the associated cost loss. This analysis can be beneficial for the construction of a new plant and for analyzing the existing system to enhance the performance. Various optimization techniques are used for this analysis for predicting the thermoeconomic potential and possible efficiency improvement in real systems. In this work, a shrouded type wind energy system is analyzed using the combination of neural network and optimization technique. The weight values of Deep learning neural network are converted into binary values using binary cuckoo search for the optimization. The results are compared with the previous experimental work and advantage of this technique is proved based on the sensible results obtained. Also the technique is compared with other optimization techniques. Henceforth this method can be used for predicting thermoeconomic of any energy systems with lest error.

Shrouded wind turbine, Energy system optimization, DLNN, BCS, Thermoeconomic analysis, RBM.