• Khalil Benmouiza Laboratory of Materials, Energetic Systems, Renewable Energies and Energy Management, Amar Teledji University of Laghouat, Algeria


Solar irradiation, ANFIS, grid partitioning, fuzzy C-means, subtractive clustering


Solar energy occupies an important place among the various sources of renewable energy. A precise knowledge of the distribution of solar irradiation in a specified location is needed before any solar irradiation system installation. This paper introduces a nonlinear clustering, adaptive-network-based fuzzy inference system (ANFIS) model to estimate the hourly solar irradiation data using meteorological inputs and clustering algorithms: grid partitioning, subtractive clustering, and fuzzy c-means. Comparing these clustering algorithms is investigated to classify the inputs into clusters, which helps the solar irradiation estimation model build better. This method's advantage is understanding and simplifying the nonlinearity presented in the input’s datasets. Moreover, the FCM algorithm gives the best results from comparing the testing data; the RMSE is 43.2274 W/m2, and MSE equals 2001.34 W/m2 with an R2 equal to 0.9893.


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