Abstract
Predicting the electrical power (PE) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of PE, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the PE with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the PE and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the PE with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented.
Highlights
The accurate forecast of power generation capacity is a significant task for power plants [1]
The quality of the results showed that the water cycle algorithm (WCA), ant lion optimization (ALO), and satin bowerbird optimizer (SBO) metaheuristic algorithms benefit from potential search strategies for exploring and mapping the PE pattern
This paper investigated the efficiency of three capable metaheuristic approaches for the accurate analysis of electrical power output
Summary
The accurate forecast of power generation capacity is a significant task for power plants [1]. This task concerns the efficiency of plants toward an economically beneficial performance [2]. Due to the nonlinear effect of several factors on thermodynamic systems [3,4] and related parameters like electrical power (PE ), many scholars have updated earlier solutions by using machine learning. As a matter of fact, there are diverse types of machine learning methods (e.g., regression [5], neural systems [6,7], fuzzy-based approaches [8],) that have presented reliable solutions to various problems. The model attained 99% accuracy and was introduced as a promising approach for this purpose
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
