# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 5 Conclusions and Limitations  
In this study, we collected data from three different types of solar panels for the city of Duzce in Turkey and trained an ANN and an LSTM to accurately predict the solar radiation using PV historical data as well as meteorological data. Data were collected for the years between 2014 and 2018 on a daily basis with a 5-min interval. The first model was an ANN model which is frequently used for solar prediction according to the literature. The second model was LSTM which is based on RNNs and is getting more utilization in time series forecasting studies. In the proposed study, we demonstrate the feasibility of accurately predicting solar radiation after 24 h if 15 h of PV historical data along with one previous day of meteorological data are provided to the LSTM. The ability of the LSTM to utilize the historical values of the features allows it to outperform other deep learning models in time series applications. Moreover, we conducted a comparison between our results and similar work in the literature in terms of many error metrics.  
Two main limitations of the proposed study would be training the models on data collected solely from the city of Duzce in Turkey. For future work, we plan to collect data from different places in Turkey, or around the globe if possible, to study the generalizability of a trained LSTM model to be used as a prediction tool for solar radiation in different locations. We are aware of the fact that the weather in Duzce is stable most of the time and it perhaps assisted in creating a very accurate model; thus, we are planning to acquire data from places where the weather is more turbulent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **[38]** & **[58]** & **Proposed Method** \\ \hline R\({}^{2}\) & 0.426 & 0.916 & 0.93 \\ \hline RMSE & 0.011 & 2.138 & 0.089 \\ \hline MBE & NA & 0.3874 & 0.009 \\ \hline MAE & 0.074 & 1.781 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison for ANN.  
Figure 9: Radiation of 175 days (testing days) as LSTM predicted (blue) versus the ground truth as measured (red).  
**Author Contributions:** Conceptualization, T.O. and B.O.A.; methodology, T.O.; software, T.O. and B.O.A.; validation, J.M.Z. and O.T.O.; formal analysis, J.M.Z. and O.T.O.; investigation, T.O.; resources, T.O.; data curation, T.O. and B.O.A.; writing--original draft preparation, T.O.; writing--review and editing, T.O., F.T. and J.M.Z.; visualization, T.O.; supervision, J.M.Z. and O.T.O.; project administration, T.O.; funding acquisition, The Scientific and Technological Research Council of Turkey (TUBITAK). All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Scientific and Technological Research Council of Turkey (TUBITAK), grant number 1059B141800505.  
Not applicable.  
Data Availability Statement: Data utilized in this study were obtained from the Duzce University using the aforementioned described solar panels. Those solar panels were installed in the Duzce University for scientific research in 2013. Data were recorded and saved in the Duzce University database. Moreover, meteorological data were obtained via a protocol between the physics department, Duzce University, and the Ministry of Metrology in Turkey. Data are available upon request.  
This study was supported by 1059B14180050 from The Scientific and Technological Research Council of Turkey (TUBITAK).  
There is no conflict of interest.