AI-DRIVEN OPTIMIZATION OF PEROVSKITE SOLAR CELLS FOR SUSTAINABLE ENERGY DEVELOPMENT IN PAKISTAN

Abstract

The findings confirm that AI-integrated PSC systems can substantially contribute to improving renewable energy generation efficiency, reducing dependency on fossil fuels, and supporting Pakistan’s long-term energy security and sustainability goals.

The transition toward sustainable and low-carbon energy systems has intensified global research into high-efficiency photovoltaic technologies. Perovskite solar cells (PSCs) have emerged as a promising alternative to conventional silicon-based photovoltaics due to their high power conversion efficiency, low-cost fabrication potential, and tunable optoelectronic properties. However, challenges such as environmental instability, thermal degradation, ion migration, and limited long-term operational reliability continue to hinder large-scale commercialization. Artificial Intelligence (AI), including machine learning, deep learning, and predictive analytics, has recently demonstrated strong potential in accelerating materials discovery, optimizing device architectures, and improving photovoltaic performance prediction. This study investigates the role of AI-driven optimization in enhancing the efficiency, stability, and operational performance of PSCs, with a specific focus on sustainable energy development in Pakistan. A quantitative explanatory research design was employed using data from 350 professionals working in renewable energy, artificial intelligence, and photovoltaic-related fields. Data were analyzed using Structural Equation Modeling (SEM) and regression techniques. The results revealed that AI capability significantly enhances PSC optimization, which in turn strongly influences sustainable energy development. The model explained 72.4% of the variance in sustainability outcomes, indicating strong predictive validity.