Estimation of Türkiye’s Solar Panel Waste Using Artificial Neural Networks (ANNs): A Comparative Analysis of ANNs and Multiple Regression Analysis

Kenan Koçkaya ()
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Kenan Koçkaya: Department of Motor Vehicles and Transportation Technologies, Divriği Nuri Demirağ Vocational School, Sivas Cumhuriyet University, Sivas 58140, Türkiye

Sustainability, 2025, vol. 17, issue 9, 1-14

Abstract: Due to global changes, interest in solar energy is increasing day by day. The share of solar energy in energy production is constantly increasing, replacing limited resources such as oil and gas, due to the fact that its source is inexhaustible and free and it does not emit CO 2 . The increasing prevalence of photovoltaic (PV) technology has brought about the problem of disposing of end-of-life panels in an environmentally friendly manner. In this study, a two-stage system model was developed to estimate Türkiye’s PV panel waste amount up to 2050. First, a new Artificial Neural Network (ANN) model was developed to estimate Türkiye’s total PV panel installed power in the coming years. The performance of the ANN model was compared with PV panel installed power estimation data obtained using multiple regression analysis. In the second stage, a mathematical model was created to estimate the amount of PV module waste. In the waste potential estimations for both methods, end-of-life and early failure scenarios due to various reasons were taken into account. As a result of the study, it was found that Türkiye’s total waste potential aligns with the future projection data published by the International Energy Agency (IEA) and the International Renewable Energy Agency (IRENA).

Keywords: solar energy; solar panel; photovoltaic technology; waste; artificial neural network; multiple regression analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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