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• W4313596593 startingPage "e12804" @default.
• W4313596593 abstract "The efficiency and performance of solar tower power are greatly influenced by the heliostats field. To ensure accurate tracking of reflectors often requires an evaluation of the beam reflected positions. This operation is costly time-consuming due to the number of heliostats. It is also necessary to set up a fast and less expensive method able to evaluate tracking heliostat. In this paper, prediction models based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) were applied to estimate rapidly and accurately heliostat error tracking. The modeling is based on the experimental data of seven different days. The input parameters are time and day number and the output is the beam reflected position following the altitude and azimuth axes. Both techniques have been able to predict the beam reflected position. A comparison of results showed that intelligent methods recorded better performance than conventional model based on geometric errors. For ANFIS model, coefficients of correlation (R2) of 0.97 is obtained compared to that of the ANN, 0.96 and 0.92 for altitude and azimuth axes respectively. The intelligent methods may be a promising alternative for predicting heliostat beam reflected the position." @default.
• W4313596593 created "2023-01-06" @default.
• W4313596593 creator A5006725744 @default.
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• W4313596593 date "2023-01-01" @default.
• W4313596593 modified "2023-10-17" @default.
• W4313596593 title "ANFIS and ANN models to predict heliostat tracking errors" @default.
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• W4313596593 doi "https://doi.org/10.1016/j.heliyon.2023.e12804" @default.
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