On the application of symbolic regression in the energy sector: Estimation of combined cycle power plant electrical power output using genetic programming algorithm

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@Article{ANDELIC:2024:engappai,

• author = "Nikola Anelic and Ivan Lorencin and Vedran Mrzljak and Zlatan Car",
• title = "On the application of symbolic regression in the energy sector: Estimation of combined cycle power plant electrical power output using genetic programming algorithm",
• journal = "Engineering Applications of Artificial Intelligence",
• volume = "133",
• pages = "108213",
• year = "2024",
• ISSN = "0952-1976",
• DOI = "doi:10.1016/j.engappai.2024.108213",
• URL = "https://www.sciencedirect.com/science/article/pii/S0952197624003713",
• keywords = "genetic algorithms, genetic programming, Averaging ensemble, Bland-Altman analysis, Combined cycle power plant, Random hyperparameter values search method",
• abstract = "This paper focuses on the estimation of electrical power output (Pe) in a combined cycle power plant (CCPP) using ambient temperature (AT), vacuum in the condenser (V), ambient pressure (AP), and relative humidity (RH). The study stresses accurate estimation for better CCPP performance and energy efficiency through responsive control to changing conditions. The novelty lies in applying genetic programming (GP) on a publicly available dataset to generate Symbolic Expressions (SEs) for high-accuracy Pe. To address the challenge of numerous GP hyperparameters, a random hyperparameter values search method (RHVS) is introduced to find optimal combinations, resulting in SEs with higher accuracy. SEs are created with varying input variables, and their performance is evaluated using multiple metrics (coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), Kling-Gupta Efficiency (KGE), and Bland-Altman (B-A) analysis). A key innovation involves combining the best SEs through an Averaging ensemble (AE), leading to a robust estimation accuracy. Notably, the AE YVE-2 achieves the highest (Pe) accuracy, including R2=0.9368, MAE=3.3378, MSE=18.4800, RMSE=4.2985, MAPE=0.7354percent, and KGE=0.9479. The investigation highlights AT as the most influential variable, underscoring the importance of choosing inputs aligned with physical processes. This paper's outlined procedure, combining GP, hyperparameter optimization, and ensemble techniques, offers an efficient method for estimating Pe in CCPP. It promises simplicity and effectiveness in real-world applications. B-A analysis proves valuable for SE selection, enhancing the proposed methodology",

}

Genetic Programming entries for Nikola Andelic Ivan Lorencin Vedran Mrzljak Zlatan Car

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