ANN-based modelling and prediction of daily global solar irradiation using commonly measured meteorological parameters

International Conference on Renewable Energies and Energy Efficiency 2017

Marzouq, M.^1 ; Bounoua, Z.^2 ; Mechaqrane, A.^2 ; Fadili, H.E.^1 ; Lakhliai, Z.^1 ; Zenkouar, K.^3

Laboratory of Computer Sciences and Interdisciplinary Physics, National School of Applied Sciences, USMBA, Fez, PO. Box 72, Fez, Morocco^1

Laboratory of Renewable Energies and Intelligent Systems, Electrical Engineering Department, Faculty of Sciences and Technologies, USMBA, Fez, PO. Box 2202, Fez, Morocco^2

Intelligent System and Application Laboratory, Faculty of Sciences and Technologies, USMBA, Fez, PO. Box 2202, Fez, Morocco^3

关键词: Coefficient of determination;    Faculty of science;    Feed-forward back propagation;    Global solar irradiation;    Input parameter;    Mean absolute percentage error;    Meteorological parameters;    Root mean square errors;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/161/1/012017/pdf
DOI  :  10.1088/1755-1315/161/1/012017

来源: IOP

【 摘 要 】

The aim of this work is to develop an artificial neural network (ANN) based model for accurately predicting the daily global solar irradiation in the city of Fez. The potential of the developed model is verified and appraised through the local collected database for the period 2009-2015 from the radiometric station of the Faculty of Sciences and Technology of Fez. The obtained model is MLP with feed forward back-propagation algorithm containing three input parameters and a single hidden layer with nine neurons. Coefficient of determination R2, the mean absolute percentage error MAPE and the relative root mean square error RRMSE are respectively equal to 97.16%, 21.77% and 18.79%.