**1. Introduction**

*1.1. Background*

In recent years, the role of energy in the life standard of human beings has been vitally important [1–3]. As the human population increases, energy demands increase exponentially [2–5]. Researchers demonstrate that the energy demand is anticipated to be approximately 1.5–3 times by 2050 [2,6,7]. Given that fact, we can anticipate that fossil fuels such as petroleum, natural gas, and coal, which are the traditional energy sources, will be depleted very soon. One more reason to switch to renewable energy is how harmful the fossil fuels are to the environment [4,8]. It should be emphasized that consumption of energy from fossil fuels is increasing CO2 (carbon dioxide) and greenhouse gas (GHG) emissions all over the world [6,9]. Increasing GHGs cause a rising atmospheric temperature of the Earth's surface [7–13]. With this concern, renewable energy has come into question for the last century [2–5,7–13].

Alternatively, solar energy, which is among renewable energy sources, is abundant and environmentally friendly, and photovoltaic (PV) technology has provided development

**Citation:** 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

Academic Editor: Luis Hernández-Callejo

Received: 9 January 2022 Accepted: 21 March 2022 Published: 28 April 2022

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and discovery for both rural and urban choices on a global scale [5,9–17]. The history of modern PV energy is based on Alexandre Becquerel's 1839 observation of the photoelectric effect [13–17]. However, after the 1990s, studies on PV energy rapidly improved [5,18]. In addition, annual PV solar energy exceeded that of wind power for the first time and reached about 70 GW, and was even 50% higher than in the previous year [18]. The global solar PV capacity reached at least 303 GW (48% compared to 2015) at the end of 2016 [18,19]. Furthermore, reports from the world's solar photovoltaic electricity supplies anticipate that PV technologies will increase to 345 GW and 1081 GW by 2020 and 2030, respectively [1,5,12,19].

The rapid expansion of PV systems does not only provide economic benefits to the electrical systems but also contributes to the reduction of global heating problems [19]. Although a solar PV system can operate by itself, a grid-connected system is required in order to reliably evaluate the electricity generation system [20,21]. Nonetheless, the instability of weather conditions and solar radiation lead to the instability of the power produced by PV panels, which causes a lot of problems in the control and operation of grid-connected PV panels [22–24]. To solve the instability problems, researchers have been developing methods to predict the output power of PV panels based on historical data and meteorological data [25,26]. Recently, artificial neural networks (ANNs) have been used to improve the prediction power of PV panels' output. ANNs have been utilized to solve further problems such as estimating radiation amount, solar power, and ambient temperature parameters [26,27]. ANNs have been applied for the modeling, identification, optimization, prediction, and control of complex systems. Hence, several studies report using ANNs in solar radiation modeling and prediction. Most of those studies utilized the geographical coordinate and meteorological data such as relative humidity, air temperature, pressure, sunshine duration, etc. as an input to the ANNs for estimating of global solar radiation [26,27]. In the following subsection, we go through some of the relevant literature to demonstrate the attempts to predict solar radiation using machine learning.
