Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
Abstract
:1. Introduction
1.1. Background
1.2. Literature Review
1.3. The Proposed Study
2. Materials and Methods
2.1. Dataset
2.2. Deep Neural Network Approaches
2.2.1. Conventional Deep ANN/Multilayer Perceptron (MLP)
2.2.2. Recurrent Neural Network (RNN)
2.2.3. LongShortTerm Memory (LSTM)
2.3. Activation Functions
3. Experimental Design
3.1. Dataset Description
3.2. Description of the ANN Model
3.3. Description of the LSTM Model
3.4. Error Measures
4. Results and Discussion
5. Conclusions and Limitations
Author Contributions
Funding
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Authors and Reference  Case Study  Research Objective  Data  Models Used  Performance of Models 

A. Mellit et al. [28]  Trieste, Italy  Estimate the amount of solar radiation for 24h using gridconnected photovoltaic plants (GCPV).  From July 1st 2008 to May 23rd 2009 for solar radiation, from November 23rd 2009 to January 24th 2010 for air temperature data.  ANN  The correlation coefficient was 98–99% for sunny days and 94–96% for cloudy days. 
C. Voyant et al. [29]  Mediterranean Sea: Ajaccio, Bastia, Montpellier, Marseille, and Nice  Estimate the global solar radiation with two models.  Data on an hourly basis from October 2002 to December 2008 and from French meteorological organization. 


A. Sozen et al. [30]  17 different cities in Turkey 
 The data were collected from 17 meteorological stations between 2000 and 2002.  ANNs 

A. Mellit et al. [31]  In Tahifet, south Algeria  They presented an application of an RNNbased approach to estimate the daily electricity generation of a photovoltaic power system (PVPS).  The measured weather data and the output of electrical signals (voltage and current) were recorded at the PVPS station in Algeria from 1992 to 1997.  ANN and RNN 

J. M. S. de Araujo [32]  Gifu, Japan  For hourly solar radiation prediction. 
 LSTMWRF (weather research and forecasting) 

A. Alzahrani et al. [33]  Canada  Estimate solar irradiance using a deep neural network.  The data were recorded for four days, from Canada’s natural sources. 
 RMSE:

A. Rai et al. [34]  Three different geographical regions in different climatic zones.  For midterm solar radiation estimation.  The data came from three different geographical regions in different climatic zones between 2014 and 2015 years. 
 For CNNBiLSTM

J. H. Yousif et al. [35]  Many different locations around the world.  Some different ANN techniques to estimate the photovoltaic thermal (PV/T) energy.  Data were taken from 2008–2017 for locations with different latitudes and climates.  Some models:
 They gave error results such as MAPE, MSE, RMSE, MBE, MPE, and R^{2}. 
Y. Jung et al. [36]  South Korea  To predict the amount of PV solar power.  The data were obtained from 164 PV plants for 63 months.  RNNLSTM 

M. Mishra et al. [37]  Urbana Champaign, Illinois  To forecast a shortterm solar power using various time intervals (1 day, 15 days, 30 days, 60 days ahead forecasting).  The datasets from February 2016–August 2017 and September 2017–October 2017. 
 They gave error results such as RMSE, MAE, MAPE, and R^{2}. 
S. Ghimire et al. [38]  Australia  Propose a convolutional longshortterm memory (CLSTM) neural network hybrid model to predict halfhourly global solar radiation (GSR).  Data from 1 January 2006 to 31 August 2018.  Some models:


D. Lee et al. [39]  Gumi city in South Korea  Build three different deep learning models to predict the solar power output of PV panels.  Data were a PV power output dataset for 39 months (from 1 June 2013 to 31 August 2016) from a PV operator located in Gumi city in South Korea. 
 LSTMbased model performs better by more than 50% compared to the conventional statistical models in terms of mean absolute error. 
Z. Pang et al. [40]  Tuscaloosa, Alabama, United States  Create two models using a shallow ANN and an RNN to estimate the solar radiation.  The data utilized wereonly meteorologicaldata from a localweather station in Tuscaloosa, Alabama, United States 
 They gave error results of RMSE and NMBE for both models. 
Dates dd.MM.yyyy HH:mm  P_{1} Amorphous ThinFilm Silicon (kWh)  P_{2} Polycrystalline Silicon (kWh)  P_{3} Monocrystalline Silicon (kWh)  Average Atmospheric Temperature $\left(\widehat{\mathit{A}}\mathbb{C}\right)$  Radiation Amounts $(W/{m}^{2})$  Panels Temperature $\left(\widehat{\mathit{A}}\mathbb{C}\right)$ 

01.01.2014 11:50  454.81  600.56  613.59  7.40  55.00  7.70 
01.01.2014 11:55  454.82  600.57  613.60  7.40  56.00  7.70 
01.01.2014 12:00  454.83  600.58  613.61  7.40  56.00  7.70 
01.01.2014 12:05  454.84  600.58  613.62  7.40  54.00  7.60 
01.01.2014 12:10  454.84  600.59  613.62  7.50  53.00  7.60 
01.01.2014 12:15  454.85  600.60  613.63  7.50  53.00  7.70 
01.01.2014 12:20  454.86  600.61  613.64  7.50  56.00  7.70 
01.01.2014 12:25  454.87  600.62  613.65  7.60  56.00  7.80 
01.01.2014 12:30  454.88  600.62  613.65  7.50  56.00  7.80 
Months  

Days  Jan.  Feb.  Mar.  Apr.  May  Jun.  Jul.  Aug.  Sept.  Oct.  Nov.  Dec. 
1  8.0  7.0  6.4  1.5  4.6  6.2  4.4  2.5  5.0  1.2  7.0  7.7 
2  7.0  1.1  6.9  4.4  5.2  5.8  4.1  5.2  3.8  2.9  7.0  7.0 
3  7.0  0.0  5.9  4.3  4.2  6.0  5.3  6.8  5.4  5.7  3.7  6.5 
4  6.4  3.1  6.7  2.7  6.1  6.0  6.1  5.2  4.7  7.0  0.8  6.3 
5  4.6  0.7  4.6  5.7  6.3  7.0  4.0  4.3  6.8  5.5  1.9  7.2 
6  5.4  0.9  4.3  5.8  6.8  7.0  0.6  1.9  6.3  6.7  0.8  6.4 
7  3.6  0.2  3.8  5.3  5.4  6.3  1.1  6.1  3.8  3.8  0.5  6.2 
8  0.0  0.6  7.9  6.8  6.7  6.3  1.6  5.6  5.4  3.0  3.0  6.4 
9  7.6  5.7  8.0  0.8  6.8  4.9  0.0  5.8  4.6  6.6  5.4  6.8 
10  8.6  6.4  8.0  1.2  6.6  3.0  1.9  2.7  5.0  4.8  5.9  6.8 
Epochs  500 
Batch size  16, 32, 64, 128, 256, 512, 1024 
Learning Rate (LR)  [0.0005, 0.05] with step 0.005 
Epochs  500 
Batch size  16, 32, 64, 128, 256, 512, 1024 
n  1, 2, 5, 10, 15, 20, 30 h 
Method  MSE  

ANN Model  Minimum training loss  0.0762 
Minimum testing loss  0.0775  
LSTM (Deep Learning)  Minimum training loss  0.0049 
Minimum testing loss  0.0080 
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Ozdemir, T.; Taher, F.; Ayinde, B.O.; Zurada, J.M.; Tuzun Ozmen, O. Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction. Appl. Sci. 2022, 12, 4463. https://doi.org/10.3390/app12094463
Ozdemir T, Taher F, Ayinde BO, Zurada JM, Tuzun Ozmen O. Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction. Applied Sciences. 2022; 12(9):4463. https://doi.org/10.3390/app12094463
Chicago/Turabian StyleOzdemir, Tugba, Fatma Taher, Babajide O. Ayinde, Jacek M. Zurada, and Ozge Tuzun Ozmen. 2022. "Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction" Applied Sciences 12, no. 9: 4463. https://doi.org/10.3390/app12094463