A novel approach for global solar irradiation forecasting on tilted plane using Hybrid Evolutionary Neural Networks

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.

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Estimation of monthly average daily global solar irradiation using artificial neural networks

Solar Energy ◽

10.1016/j.solener.2007.06.003 ◽

2008 ◽

Vol 82 (2) ◽

pp. 181-187 ◽

Cited By ~ 131

Author(s):

J. Mubiru ◽

E.J.K.B. Banda

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Solar Irradiation ◽

Monthly Average ◽

Global Solar Irradiation ◽

Artificial Neural

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Solar Energy Prediction for Malaysia Using Artificial Neural Networks

International Journal of Photoenergy ◽

10.1155/2012/419504 ◽

2012 ◽

Vol 2012 ◽

pp. 1-16 ◽

Cited By ~ 23

Author(s):

Tamer Khatib ◽

Azah Mohamed ◽

K. Sopian ◽

M. Mahmoud

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Solar Energy ◽

Solar Irradiation ◽

Mean Bias Error ◽

Percentage Error ◽

Clearness Index ◽

Energy Prediction ◽

Global Solar Irradiation ◽

Artificial Neural

This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.

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