Abstract
This article presents different combinations of input parameters based on an intelligent technique, using neural networks to predict daily global solar radiation (GSR) for twenty-five Moroccan cities. The collected measured data are available for 365 days and 25 stations around Morocco. Different input parameters are used, such as clearness index KT, day number, the length of the day, minimal temperature Tmin, maximal temperature Tmax, average temperature Taverage, difference temperature ΔT, ratio temperature T-Ratio, average relative humidity RH, solar radiation at the top outside atmosphere TOA, average wind speed Ws, altitude, longitude, latitude, and solar declination. A different combination was employed to predict daily GSR for the considered locations in order to find the most adequate input parameter that can be used in the prediction procedure. Several statistical metrics are applied to evaluate the performance of the obtained results, such as coefficients of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), test statistic (TS), linear regression coefficients (the slope “a” and the constant “b”), and standard deviation (σ). It is found that the usage of input parameters gives highly accurate results in the artificial neural network (FFNN-BP) model, obtaining the lowest value of the statistical metrics. The results showed the best input of 25 locations, 12 inputs for Er-Rachidia, Marrakech, Medilt, Taza, Oujda, Nador, Tetouan, Tanger, Al-Auin, Dakhla, Settat, and Safi, seven inputs for Fes, Ifrane, Beni-Mellal, and Meknes, six inputs for Agadir and Rabat, five inputs for Sidi Ifni, Essaouira, Casablanca and Kenitra, four inputs for Ouarzazate, Lareche, and Al-Hoceima. In terms of accuracy, R2 of the selected best inputs parameters varies between 0.9860% and 0.9920%, the range value of MBE (%) being from −0.1076% to −0.5931%, the RMSE between 0.1990 and 0.4580%, the range value of the NRMSE between 0.0355 and 0.8938, and the lowest value MAPE between 0.0019 and 0.0060%. This technique could be used to predict other parameters for locations where measurement instrumentation is unavailable or costly to obtain.
Highlights
In the last decade, energy demand has been growing internationally, with the rise in population growth and economic development
In Algeria, a simplified hybrid model was developed by Gairaa et al (2016) using the linear autoregressive moving average (ARMA) model combined with Artificial Neural Network (ANN) to predict the daily global solar irradiation using measurement data from two locations
The performance of the hybrid ANN-Markov transition matrix (MTM) model was calculated to verify the accuracy of the predicted results and was given as root mean square error (RMSE) 8%, R2 varying in the range of [0.9 0.92] (Belmahdi et al, 2020a)
Summary
Energy demand has been growing internationally, with the rise in population growth and economic development. In India, Alam et al (2006) have implemented the feedforward backpropagation network (FFBPN) to predict the solar radiation, and the selected inputs were the latitude, longitude, attitude, months of years, rainfall ration, mean duration of sunshine per hour, and ratio of relative humidity. In Algeria, a simplified hybrid model was developed by Gairaa et al (2016) using the linear autoregressive moving average (ARMA) model combined with ANN to predict the daily global solar irradiation using measurement data from two locations. The output data were the daily global solar radiation and the geographical coordinates, such as latitude, longitude, and altitude, were the input data of the neural network layer. The performance of the hybrid ANN-MTM model was calculated to verify the accuracy of the predicted results and was given as RMSE 8%, R2 varying in the range of [0.9 0.92] (Belmahdi et al, 2020a). We can cite the predicted solar radiation at the horizontal and tilted surface:
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