Abstract :
Abstract In the rapidly evolving landscape of renewable energy, the integration of machine learning (ML) technologies presents unprecedented opportunities for optimization, efficiency, and sustainability. However, the successful implementation of ML in renewable energy processes requires more than technical expertise—it demands a strategic and agile approach that aligns with the principles of Scrum methodology. This paper explores the role of Scrum Masters in facilitating ML adoption within renewable energy enterprises, providing insights into their unique perspective, challenges, and strategies. Drawing upon empirical research and industry best practices, the paper elucidates a Scrum Master's approach to harnessing data for ML applications in renewable energy, offering practical guidance for navigating the complexities of agile ML development. By leveraging the principles of transparency, collaboration, and iterative improvement inherent in Scrum, renewable energy stakeholders can unlock the full potential of ML technologies, driving innovation and sustainability in the transition towards a cleaner energy future.
Keyword :
Keywords: Renewable energy, Machine learning, Scrum, Agile methodologies, Data harnessing, Optimization, Sustainability, Collaboration, Iterative improvement