AI-Driven Optimization of Renewable Energy Investments

Ahmad Hasan (), Basil Hanafi (), Mohammad Arham Khan () and Mohammad Ammar Ahsan ()
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Ahmad Hasan: Aligarh Muslim University
Basil Hanafi: Aligarh Muslim University
Mohammad Arham Khan: University of Nebraska-Lincoln
Mohammad Ammar Ahsan: Aligarh Muslim University

Chapter Chapter 17 in Green Horizons, 2025, pp 305-331 from Springer

Abstract: Abstract In the quest for a sustainable future, optimizing investments in renewable energy projects is paramount. This chapter explores the transformative role of artificial intelligence (AI) in enhancing the financial viability and efficiency of renewable energy investments. By leveraging advanced AI models, including machine learning, deep learning, and reinforcement learning, investors can optimize portfolios and make data-driven decisions that maximize returns while minimizing risks. The chapter delves into the methodologies for data collection and preprocessing, essential for training robust AI models, and examines predictive analytics techniques that forecast the financial performance of renewable energy ventures. Through detailed case studies, the chapter highlights successful AI applications in solar, wind, and other renewable energy projects, offering practical insights and lessons learned. It also addresses the challenges and limitations of deploying AI in this domain, including technical, data-related, ethical, and regulatory issues. Looking ahead, the chapter identifies emerging AI technologies and future trends poised to revolutionize renewable energy finance. By providing a comprehensive overview and practical guidance, this chapter aims to equip investors, researchers, and policymakers with the knowledge and tools needed to harness the full potential of AI in driving sustainable energy solutions.

Keywords: Artificial intelligence (AI); Renewable energy investments; Predictive analytics; Investment optimization; Sustainable finance (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-6495-5_17

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DOI: 10.1007/978-981-96-6495-5_17

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