A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface

Abstract

With the growing demand for clean and economically feasible renewable energy, solar photovoltaic (PV) system usage has increased. Among many factors, the tilt and azimuth angles are of great importance and influence in determining the photovoltaic panel’s efficiency to generate electricity. Although much research was conducted related to solar PV panels’ performance, this work critically determined the tilt and azimuth angles for PV panels in all countries worldwide. The optimum tilt and azimuth angles are estimated worldwide by the photovoltaic geographic information system (PVGIS). Also, annual and average daily solar irradiation incident on the tilted and oriented plate optimally (AR1 and DR1) are calculated. Besides, annual and average daily solar irradiation incident on plate tilt optimally and oriented because of the south in the northern hemisphere and because of the north in the southern hemisphere (AR2 and DR2) are estimated. PVGIS is also used to calculate the annual and average daily solar irradiation incident on the horizontal plate (AR3 and DR3). The data collected from PVGIS are used to develop an efficient and accurate artificial neural network model based on feed-forward neural network approach. This model is an essential subpart that can be used in an embedded system or an online system for further PV system analysis and optimization. The developed neural model reflected very high accuracy in predicting the PV panels’ optimal tilt and azimuth angles worldwide. The benefit of tilting is generally increased by increasing the latitude. As the latitude increases, the tilt factor (F) increases because of the increase in the optimum tilt angle by increasing the latitude. The optimal orientation is due to the north in the southern hemisphere and due to the south in the northern hemisphere for most cities worldwide. In sum, it can be concluded that the optimum tilt angle is equal to or greater than the latitude until the latitude 30°. The optimum tilt angle becomes less than the latitude, and the difference is increased until it reaches more than 20°. Hence in this study the aim is to develop a simple neural network model which can accurately predict the annual radiation and optimum tilt and azimuth angle in any region of the world and can be easily implemented in a low-cost microcontroller.

A Comparative Assessment for the Potential Energy Production from PV Installation on Residential Buildings

“…Three tilt angles were chosen for the simulation: (1) 27 • : Since the Optimum tilt angle for the most cities in Palestine is about 27 • [40], which is in line with the result from the study by Jacobson et al [6], but it required a relatively long distance between the arrays to minimize the shading losses;…”

Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

“…As shown in Table 34, it is clear that the number of input variables vary from one model to another, Şahiṅ, 13 has used a huge number of input and output variables, Kim et al 14 have used a relatively large number of variables compared to Abdallah et al 12 AL-Rousan et al 2 and the proposed models in this article, where, two to four variables were used. In addition, they have focused on studying dual-axis solar tracking systems.…”

“…Abdallah et al 12 have proposed a multilevel world comprehensive feed forward neural network model. This new proposed model consists of two stages of neural networks with one hidden layer in each network.…”

“…Abdallah et al have proposed a multilevel world comprehensive neural network model to predict optimum orientation and tilt angles for photovoltaic (PV) panels in all countries worldwide. 12 Meteorological data collected by the photovoltaic geographic information system (PVGIS) in 2019 are used to develop an efficient and accurate feed-forward neural network prediction model.…”

“…As shown in literature review, several research are carried out by developing the conventional neural network by changing the selected variables, 2,13 modifying the error optimization function, 3 using a reference value to the neural network inputs, 3,8 finding new generation of neural network, 4,10,12 or increasing the number of neurons in the hidden layers. 7,9 Artificial neural network proved that it is sufficient to be used as solar tracking systems controller, and effective in complex and nonlinear systems.…”