Predicting electrical power output of combined cycle power plants using a novel artificial neural network optimized by electrostatic discharge algorithm

Combined cycle power plants (CCPP) are among the most sophisticated, yet efficient, systems for producing electrical energy. Hence, simulating their performance has been an engineering hotspot toward sustainable developments. This paper employs novel soft computing methods for predicting electrical power (PE) output of CCPPs. To this end, a metaheuristic technique called electrostatic discharge algorithm (ESDA) is coupled with an artificial neural network (ANN) to create the proposed hybrid. Its performance is compared to several conventionally trained ANNs to investigate the effect of hybridization. By considering the influence of ambient temperature, exhaust vacuum, atmospheric pressure, and relative humidity, the PE is predicted through a 4 x 9 x 1 network. Among the conventional trainers, Levenberg-Marquardt emerged as the most promising one. However, the ESDA outperformed this algorithm in both training and testing phases. Accordingly, the used metaheuristic optimization could improve the robustness of the regular ANN in surmounting computational drawbacks. The ESDA-ANN is, therefore, introduced as a reliable predictive tool for the PE modeling, and the corresponding predictive formula is presented in the last part of this research.

Automated summary

This paper proposes a reliable predictive tool for the electrical power output of combined cycle power plants using novel soft computing methods. By combining the electrostatic discharge algorithm with an artificial neural network, the proposed hybrid outperforms conventional methods in both training and testing phases.