Abstract
A combined cycle power plant (CCPP) employs gas and steam turbines to generate 50% more power while utilizing the same fuel as a normal single cycle plant. The performance of a CCPP under full load is affected by a variety of factors such as weather, process interactions, and coupling, which makes it challenging to operate. Therefore, a reliable assessment of the maximum output power of a CCPP is required to improve plant reliability and monetary performance. In this paper, a predictive model based on a generalized additive model (GAM) is proposed for the electrical power prediction of a CCPP at full load. In GAM, a boosted tree and gradient boosting algorithm are considered as shape function and learning technique for modeling a non-linear relationship between input and output attributes. Furthermore, predictive models based on linear regression (LR), Gaussian process regression (GPR), multilayer perceptron neural network (MLP), support vector regression (SVR), decision tree (DT), and bootstrap-aggregated tree (BBT) are also designed for comparison purposes. Results reveal that GAM improves the RMSE by 74%, 68.8%, 70.3%, 54.8%, 21.2%, and 17.3% compared to LR, GPR, MLP, SVR, DT, and BBT, respectively. Furthermore, the results of the Man-Whitney U test and rank analysis also confirm the effectiveness of GAM for energy prediction of CCPP. Finally, it can be concluded that the proposed method is effective, robust, and accurate for the assessment of the maximum output power of a CCPP to improve plant consistency and financial performance.
Highlights
In order to analyze a thermodynamic system, various hypotheses are needed to compensate for the uncertainty in the solution
In this article, a gradient boosted generalized additive model (GAM) machine learning (ML) algorithm is proposed for the development of a predictive model for combined cycle power plant (CCPP)
Predictive models based on linear regression (LR), Gaussian process model (GPR), multilayer perceptron neural network (MLP), support vector regression (SVR), decision tree (DT), and BBT are designed for the performance comparison of GAM
Summary
In order to analyze a thermodynamic system, various hypotheses are needed to compensate for the uncertainty in the solution. These hypotheses are impractical for analyzing complex systems It involves solving hundreds of nonlinear equations, resulting in excessive computational requirements. To circumvent this constraint, machine and deep learning techniques are gaining popularity as a way to avoid thermodynamic-based techniques, discover counter-intuitive aspects, and provide performance efficiencies beyond design variables. Machine and deep learning techniques are gaining popularity as a way to avoid thermodynamic-based techniques, discover counter-intuitive aspects, and provide performance efficiencies beyond design variables These advances result from the discovery of diverse and complex correlations and interconnections between important input and output attributes [1]. Researchers have utilized various approaches based on machine learning (ML) algorithms to predict the output power at full load of CCPP.
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