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Renewable energy is seen as the way forward across the world. The shift away from reliance on finite energy sources, such as coal and oil, is a global concern. Osahon Ero, a Data Science Consultant from Nigeria, seeks to use Machine Learning (ML) to meet the challenges faced as the world transitions to renewable energy.
Osahon Ero comes from a solid engineering background. He holds a Bachelor of Engineering in Mechanical Engineering from the University of Benin, Nigeria. Hailing from Nigeria, Ero grew up dismantling gadgets, sometimes putting them back together, but more often than not, building entirely new gadgets. Exploring nature and the world around his home taught Ero to look for patterns and appreciate the natural world’s power.
Ero, who earned his Master of Science in Business Analytics from the University of Rochester in New York, began his career with Shell Petroleum Development Company, where he initially worked as a trainee in production engineering. Over time, Ero climbed through several positions within Shell, including a promotion to Operations Supervisor and then Operations Team Lead. As the Team Lead, Ero oversaw a team of 56 engineers and technicians. His leadership focused on operational excellence and safety, earning him recognition for his creative use of machine learning in production enhancements. Taking his approach to machine learning for operational safety, Ero has turned his attention to the challenges faced by renewable energy.
There is an inherent variability and unpredictability associated with renewable energy. There is a vast amount of data from various sources, like weather stations and sensors on turbines, ocean buoys, and solar panels. Managing and processing this data to be useful requires robust machine learning models (MLM) that can handle large-scale, real-time data efficiently and accurately.
Leveraging machine learning, Ero developed MLMs that predict maintenance needs and optimize operational processes. He has done this with Shell: his model predicts the quality of crude oil, impacting operational decisions and efficiency. For renewable energy, extending the lifespan of equipment will go a long way to easing the financial cost of turbines and solar farms. Allowing ML models to predict energy supply fluctuations automates smarter energy distribution, maintaining grid stability. With an aging power infrastructure, fluctuations can be dangerous. (As was seen in Texas in 2021.) Integrating renewable energy sources into existing grids is technically challenging, but ML models would balance loads and maintain a consistent energy supply.
Osahon Ero has proven that ML-driven process optimizations improve safety protocols, leading to a better working environment and reduced danger. With Shell, his model improved safety for everyone. As a bonus, his ML model enhanced process efficiencies and reduced operational costs, leading to over $300K in savings. This directly addresses one of the largest challenges faced by renewable energy: cost.
The high initial costs for setting up ML systems could be considered another challenge for renewable energy. Yet Osahon Ero has developed detailed cost-benefit analyses for ML projects, demonstrating their long-term savings and efficiency gains. Renewable energy is the way forward, and Ero has shown that ML models are a cost-effective way to make renewable energy viable.
Finally, ML technologies in renewable energy require a workforce skilled in both energy systems and data science. That’s a pretty rare combination. Again, Ero has thought of that, too, by organizing and supporting ongoing education and training programs to upskill existing staff. He also collaborates with academic institutions to tailor educational programs that prepare future employees with the needed skills in both ML and renewable energy.
Osahon Ero is confident that the growing renewable energy sector will benefit from ML systems. His ML models have proven themselves, and as the powers move forward in implementing ML throughout energy production, his impact on ML in renewable energy will be a good one.
