# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 1 Introduction
### The Proposed Study  
Based on the aforementioned literature review, we found that data from PV panels and/or meteorological data are utilized to predict solar radiations. The highest achievable results were found by deep learning techniques [28; 31; 36; 37; 38; 39; 40; 41; 42; 43; 44]. Therefore, we designed our experiment based on shallow and deep learning models. The motivation behind the proposed study was the irregularity of energy delivery in Duzce city in Turkey, which may exist in similar cities around the world. We utilized both PV historical data, which was collected from the city of Duzce in Turkey for the period between 2014 to 2018, as well as the daily meteorological data for the same period. In the proposed study, we compared between a deep ANN and an LSTM model in terms of predicting the solar radiation in the city of Duzce in Turkey on daily basis. We performed hyperparameter optimization at predefined hyperparameter values for both the networks, ANN and LSTM. Selecting a deep learning architecture to perform an accurate prediction of the solar radiation amount is crucial for the system operators to reduce costs and uncertainties [17; 41; 42; 43; 44]. The main contributions of the proposed work can be summarized as: (i) conducting a comparison between the performance of the most common deep learning models in the literature, (ii) building an LSTM to accurately predict the solar radiation at the city of Duzce in Turkey with the potential to be generalized to more cities around the world, and (iii) conducting a comparison between our results in terms of the coefficient of determination (R\({}^{2}\)), root mean squared error (RMSE), mean biased error (MBE), and mean absolute error (MAE). \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors and Reference** & **Case Study** & **Research Objective** & **Data** & & **Models Used** & **Performance of Models** \\ \hline \hline \multirow{6}{*}{[PERSON] et al. [23]} & \multirow{2}{*}{CCCards} & \multirow{2}{*}{Cards} & \multirow{2}{*}{Estimate solar irradiance using a deep neural network.} & \multirow{2}{*}{The data were recorded for four days, from Cards’s natural sources} & & RMSE- \\  & & & & & & \(\bullet\) LSTM = 0.06 \\  & & & & & & \(\bullet\) LSTM = 0.06 \\  & & & & & & \(\bullet\) LSTM = 0.11 \\  & & & & & & \(\bullet\) LSTM = 0.16 \\  & & & & & & \(\bullet\) LSTM = 0.04 \\  & & & & & & \(\bullet\) RNN = 0.005 \\ \hline \multirow{6}{*}{[PERSON] et al. [24]} & \multirow{2}{*}{Cards} & \multirow{2}{*}{The three different geographical regions in different domains,} & \multirow{2}{*}{For each column four days, from person between 2014 and 2015 years,} & \multirow{2}{*}{The data were recorded for four days, from \#} & \multirow{2}{*}{The data were recorded for four days, from \#} & \multirow{2}{*}{The observation (SNA)} & \multirow{2}{*}{For CNN-BLSTM} \\  & & & & & & \(\bullet\) LSTM = 0.054 \\  & & & & & \(\bullet\) LSTM = 0.057 \\  & & & & & & \(\bullet\) LSTM = 0.057 \\  & & & & & & (Dx) model \\ \hline \multirow{6}{*}{[PERSON] et al. [35]} & \multirow{2}{*}{Many different locations around the world,} & \multirow{2}{*}{Some different ANN techniques to estimate the photosynthetic chemical (PV/T) energy & Data were taken from 2008-3017 for locations with different locations and different locations.} & \multirow{2}{*}{Data were taken from 2008-3017 for locations with different locations and different locations.} & \multirow{2}{*}{Inventually model, (DNN)} & \multirow{2}{*}{They gave more unseen methods such as MAVE, MNE, RMSE, MNE, MNE, MNE, and R\({}^{2}\).} \\  & & & & & & \(\bullet\) LSTM = 0.07 \\  & & & & & & \(\bullet\) LSTM \\ \hline \multirow{6}{*}{[PERSON] et al. [24]} & \multirow{2}{*}{South. Koma} & \multirow{2}{*}{To predict the amount of PV} & \multirow{2}{*}{The data were obtained from 164 PV plants for 3D models.}