# Prediction Model for the Performance of Different PV Modules Using Artificial Neural Networks
Offline methods are mainly based on genetic algorithms (GA) and artificial intelligence (AI) techniques. The major drawback of some of the offline methods is the ability to detect unusual conditions for the solar cell system. Some researchers have discussed this issue in their research. [PERSON] et al. [21] used the five-parameter model, while [PERSON] et al. [22] used the Levenberg-Marquardt method. These studies proved their high accuracy and lower testing time than online methods. Much research, such as that of [PERSON] et al. [23], used numerical techniques. However, these studies faced different challenges, such as complex calculations and the relationship between accuracy and initial conditions. Due to the drawbacks of some offline methods, a variety of methods, including genetic algorithms (GA) [24; 25] and hybridized evolutionary methods [26; 27], were used to solve them. These methods (called \"evolutionary algorithms\") extract the IV curve with different approaches and concepts. Table 1 highlights the accuracy of these methods. Researchers used some AI technologies, such as artificial neural networks (ANNs), to predict different parameters related to PV performance. One of the parameters is solar radiation. Solar radiation in tropical regions such as Malaysia is unique because it is stable and does not change throughout the year [28; 29]. [PERSON] et al. [30] created a method for predicting it in Malaysia using ANNs. The technique that was proposed had a low percentage error compared to the previous process. In addition, [PERSON] [31] investigated the potential for the ANN with ant colony optimization (ACO) to predict received solar radiation. [PERSON] et al. [32] proposed a model to improve solar forecasting using ANNs with fuzzy logic. The model had a lower MAPE compared to models with no fuzzy logic. [PERSON] et al. [33] used ANNs to predict the IV curve. The proposed method had a high percentage of errors in predicting the IV curve. [PERSON] et al. [34] predicted the IV characteristic curve using ANNs with an explicit analytical model (EAM). [PERSON] et al. [35] predicted the IV parameters (V\({}_{oc}\), I\({}_{sc}\), maximum current (I\({}_{m}\)), maximum voltage (V\({}_{m}\)), and P\({}_{m}\)) using ANNs for different types of PV modules, while [PERSON] et al. [36] predicted the IV curve using the random forest method. Some researchers used ANNs to predict the output power of the PV system. [PERSON] [37] predicted the output power using ANNs. Meanwhile, [PERSON] et al. [38] used a recurrent neural network (RNN) to predict the output power; the mean absolute percentage error for the method was about 10.805%. Moreover, [PERSON] et al. [39] indicated the output power in Qatar using ANNs in two different techniques. The main objective of this study is to compare the predicting performance (FF, efficiency, and IV curve) for six different types of PV module (a CIGS module, a flexible monomodule, a thin-film module, a monocrystalline module, a polycrystalline module, and a flexible back-contact monomodule) under test conditions (solar radiation and ambient temperature) in Malaysia using GRNNs. The proposed model uses simple inputs for both networks, such as I\({}_{sc}\), V\({}_{oc}\), P\({}_{m}\), T\({}_{c}\), and irradiance, which could be extracted by any IV analyzer devices, and achieves this with higher accuracy than the online and offline methods represented in Table 1.