Evaluation of machine learning-based applications in forecasting the performance of single effect absorption chiller network

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@Article{PANAHIZADEH:2021:TSEP,

• author = "Farshad Panahizadeh and Mahdi Hamzehei and Mahmood Farzaneh-Gord and Alvaro Antonio Ochoa Villa",
• title = "Evaluation of machine learning-based applications in forecasting the performance of single effect absorption chiller network",
• journal = "Thermal Science and Engineering Progress",
• volume = "26",
• pages = "101087",
• year = "2021",
• ISSN = "2451-9049",
• DOI = "doi:10.1016/j.tsep.2021.101087",
• URL = "https://www.sciencedirect.com/science/article/pii/S2451904921002481",
• keywords = "genetic algorithms, genetic programming, Absorption chiller network, Machine learning, Coefficient of performance, Thermal energy consumption",
• abstract = "The present study aims to predict the coefficient of performance and thermal energy consumption of an absorption chiller network, using three widely-used machine learning methods of the artificial neural network, support vector machine, and genetic programming. To this aim, a case study was conducted on the Marun petrochemical company in Iran. The genetic programming was used to estimate new formulas for the functions in terms of operational variables. Then, using the optimization algorithm, the optimal load of each chiller in the network was obtained. The results revealed that the artificial neural network technique has the highest prediction accuracy among the mentioned methods, in which the mean square errors of the performance coefficient and thermal energy consumption of chiller are 1.683 times 10-8 and 8.157 times 10-8, respectively. Also, for the support vector machine and genetic programming methods mean square errors are 1.627 times 10-3, 1.135 times 10-3 and 2.187 times 10-3, 4.358 times 10-3, respectively. The new estimated formulas for the performance coefficient and thermal energy consumption of each chiller based on the genetic programming have acceptable accuracy and their coefficients of determination are 0.97093 and 0.95768, respectively. Moreover, given the constant operating variables, if the cooling load of each chiller in the network is optimally selected, the thermal energy consumption of the network will decrease averagely by 2.1 percent and the performance coefficient of the network will increase by 1.3 percent",

}

Genetic Programming entries for Farshad Panahizadeh Mahdi Hamzehei Mahmood Farzaneh-Gord Alvaro Antonio Ochoa Villa

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