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- W3209173072 abstract "In solar tower power plants, calibration is an essential and necessary operation to ensure accurate control of heliostat field. The calibration process needs a lot of time, especially if the number of heliostats is important. In this paper, the Adaptive Neuro-fuzzy Inference System (ANFIS) was applied to predict the heliostat tracking errors. To test and train these different models, experimental data were used, with as input the day number and time and output tracking error (azimuth or altitude). Experiments were carried out on three different days between 10 AM and 3 PM. Several scenarios with different numbers and shapes of membership functions were designed to select the optimal model. The results obtained show very high prediction rates depending on the number and shape of membership functions output. Mean square error (MSE) of 0.0074 and 0.038 and prediction rate of 99.65% and 95.53% are obtained respectively for azimuth and altitude axis." @default.
- W3209173072 created "2021-11-08" @default.
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- W3209173072 date "2021-01-01" @default.
- W3209173072 modified "2023-10-16" @default.
- W3209173072 title "Prediction of Heliostat Tracking Errors Using an Adaptive Neuro-Fuzzy Inference System" @default.
- W3209173072 doi "https://doi.org/10.2139/ssrn.3953061" @default.
- W3209173072 hasPublicationYear "2021" @default.
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