# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 2 Materials and Methods  
### Dataset  
The solar data, which were utilized in the current study, were collected from three different types of grid-connected PV panels. The PV panels were installed on the top roof of the University of Duzce Scientific and Technological Research and Application Center (DUBIT) by Duzce University Clean Energy Resources Application and Research Center (DUTEM) in 2013 in Turkey. The geographic location of the center panel is 40\({}^{\circ}\)54\({}^{\prime}\)14.7\({}^{\prime\prime}\) N and 31\({}^{\circ}\)10\({}^{\prime}\)56.7\({}^{\prime\prime}\) E. Figure 1 shows the three different PV solar panels of schemas in DUBIT in Duzce University in Duzce.  
As shown in Figure 1, the first type of panels used (P1) is an amorphous thin film silicon panel. A single P1 panel has the power of 100 W. In the proposed study, 24 P1 panels were utilized. The 24 P1 panels were structured in the form of a matrix with two rows and twelve columns (2 \(\times\) 12). The total output power generated by the (P1) panels matrix equals 2400 W. The second type of panel (P2) is a polycrystalline silicon panel. A single P2 has a solar panel power of 240 W. Eleven P2 panels were utilized in the current study. The 11 (P2) panels were placed as a single row. That row produces a total power output of 2400 W. The third type of panel (P3) is a monocrystalline silicon panel. P3 produces a solar panel power of 235 W. Ten P3 panels were placed in a single row. Those have a total power output 2350 W. That system of panels (P1, P2, and P3) has been recording data every 5 min since 2013. Output power is recorded for each panel. Average temperature, radiation amount, and average atmospheric temperature were recorded for all panels.  
Table 2 demonstrates an example of the recorded data recorded from P1, P2, and P3. Therefore, for every day, there are 288 rows of data and 6 columns (3 columns denote the output power for each panel type (kWh), 1 column denotes average atmospheric temperature (\(\AA\)\({}^{\circ}\)C), 1 column denotes radiation amount (W/m\({}^{2}\)), and 1 column denotes panel temperature (\(\AA\)\({}^{\circ}\)C). Rows are indexed with the time of acquisition. Moreover, meteorological data were recorded on daily basis. Thus, for every 288 rows of panels' data, there is a corresponding row of meteorological data. Meteorological data acquired were as follow: daily average relative humidity, daily sunshine time, and daily average cloudiness. Meteorological data were recorded by the Ministry of Metrology in Turkey. The rationale behind using the meteorological data is to include any factor that might be affecting the radiation amount detected by the panels. Some of the meteorological data is presented in Table 3.  
Figure 1: Three different PV solar panels of schemas in DUBIT in Duzce.  
Table 3 shows an example of 10 days' data of the meteorological data that were acquired from the Meteorological Ministry of Turkey in 2014 in Duzce, Turkey. Rows represents days, columns represent months, and the values in each cell represent the average cloudiness on that day in that month and recorded average daily cloudiness for 12 months of the year. Similar tables are given for the other meteorological data. Meteorological data corresponding to 4 years from 2014 to 2018 were utilized in the current study [45].  
Data were cleaned by removing rows with missing values, then all the data were aggregated in a single table containing the meteorological data along with the panels' data. Python 3.7 and pandas were utilized for data cleaning and manipulation.