Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network

Abstract

This paper proposes a novel prediction model for photovoltaic (PV) system output current. The proposed model is based on cascade-forward back propagation artificial neural network (CFNN) with two inputs and one output. The inputs are solar radiation and ambient temperature, while the output is output current. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. The monitored performance is recorded every 2 s in order to consider the uncertainty of the system's output current. A comparison between the proposed model and other empirical and statistical models is done in this paper as well. Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. Three statistical values are used to evaluate the accuracy of the proposed model, namely, mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). These values are used to measure the deviation between the actual and the predicted data in order to judge the accuracy of the proposed model. A simple estimation of the deviation between the measured value and the predicted value with respect to the measured value is first given by MAPE. After that, the average deviation of the predicted values from measured data is estimated by MBE in order to indicate the amount of the overestimation/underestimation in the predicted values. Third, the ability of predicting future records is validated by RMSE, which represents the variation of the predicted data around the measured data. Eventually, the percentage of MBE and RMSE is calculated with respect to the average value of the output current so as to present better understating of model's accuracy. The results show that the MAPE, MBE, and RMSE of the proposed model are 7.08%, À0.21 A (À4.98%), and 0.315 A (7.5%), respectively. In addition to that, the proposed model exceeds the other models in terms of prediction accuracy.

Predicting the Output Power of a Photovoltaic Module Using an Optimized Offline Cascade-Forward Neural Network-Based on Genetic Algorithm Model

“…To validate the effectiveness of the proposed model, its performance in terms of MAPE compared with some of the research works in the same area. Here, the proposed model compared with perturb and observe (P&O) method with incremental conductance method (ICM) [38], random forests (RFs) model [4], generalized regression artificial neural network (GRNN) [30] and CFNN [30] as listed in Table 3.…”

“…The number of hidden neurons is considered one of the essential hyper-parameters in the ANN, which significantly affects the ANN models' accuracy. According to the literature, no mathematical formula can be used to find the optimal number of hidden neurons directly [30]. The number of hidden neurons is dependent on the data characterization and the number of inputs, outputs, and hidden layers.…”

Online Modelling and Calculation for Operating Temperature of Silicon-Based PV Modules Based on BP-ANN

“…The modelling for operating temperature has attracted more and more attention [10][11][12], and many researchers have explored the operating temperature calculation methods of silicon-based PV modules. Reference [13] used the theoretical model of silicon-based PV modules to calculate the operating temperature.…”

Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions

“…Their experiments and proposed model results are close to the actual results with small percentage error. Several researchers are implementing supervised feed forward ANN techniques to forecast solar energy production [11][12][13]. Artificial neural networks (ANN) have become increasingly useful for system modelling and the optimization of results.…”

“…The results of the experiments and numerical models showed that the percentage of energy saving is about 15% in North European weather conditions. Khatib et al [12] proposed a solar irradiation, system-based ANN, which uses data from 28 cities in Malaysia. The proposed neural network model is used to forecast the clearness index, which is used to establish a predictive global solar irradiation system for Malaysia.…”