The Role of Artificial Intelligence in Enhancing Renewable Energy Efficiency: A Case Study on Solar and Wind Energy Optimization
Abstract
This paper explores the transformative role of artificial intelligence (AI) in enhancing the efficiency and functionality of renewable energy systems, focusing on solar and wind energy optimization. Solar and wind energy, as key players in the global energy transition, are not just environmentally beneficial but also socially transformative, offering affordable energy solutions to underserved communities. For instance, low-income families in Pakistan increasingly adopt solar energy due to its affordability compared to traditional energy sources (Asian Development Bank [ADB], 2022). The paper highlights AI applications such as predictive maintenance, optimization of energy output, and integration with energy storage, emphasizing their potential to improve the reliability and sustainability of renewable energy systems. Concrete examples include AI-powered solar panel tracking systems increasing efficiency by 20% (Massachusetts Institute of Technology [MIT], 2021), Google’s DeepMind predicting wind power output 36 hours in advance to enhance value by 20% (Google, 2019), and a Danish wind farm utilizing AI to optimize layout, achieving a 12% increase in energy production (Technical University of Denmark, 2020). The research underscores AI’s role in not only driving technical innovation but also addressing global energy inequities.
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Data Availability Statement
Data Availability Statement:
The data supporting the findings of this study are available from publicly accessible databases, including reports from the International Renewable Energy Agency (IRENA), the International Energy Agency (IEA), and case studies from the National Renewable Energy Laboratory (NREL). Additional data generated during the analysis, such as AI-based optimization models and algorithms, can be provided by the authors upon reasonable request.
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