Scrumming through Machine Learning: A Guide for Renewable Energy Enterprises


  • Larry Brandon, Patrick Aaron Department of Engineering, Tulane State University


Renewable energy, Machine learning, Scrum, Agile methodologies, Integration, Innovation, Efficiency, Sustainability, Cross-functional collaboration, Continuous improvement


As renewable energy enterprises navigate the complexities of integrating machine learning (ML) technologies into their operations, the role of Scrum methodologies becomes increasingly prominent. This paper provides a comprehensive guide for renewable energy enterprises seeking to leverage ML effectively, drawing upon the principles of Scrum to facilitate agile development and implementation processes. Through a synthesis of industry best practices and empirical insights, the paper elucidates key strategies, challenges, and opportunities associated with ML integration in renewable energy contexts. Practical guidance is offered for Scrum Masters and stakeholders, emphasizing the importance of cross-functional collaboration, iterative development, and continuous improvement. By embracing an agile approach informed by Scrum principles, renewable energy enterprises can unlock the transformative potential of ML technologies, driving innovation, efficiency, and sustainability in the transition towards a cleaner and more resilient energy future.