CODE: RE26
DURATION: 5 Days/10 Days
CERTIFICATIONS: CPD
This course infuses artificial intelligence into renewable energy and provides participants with practical knowledge and strategic insights into how artificial intelligence and advanced analytics can transform the maintenance of renewable energy infrastructure. It explores the application of machine learning, IoT-enabled sensors, and real time data modeling to anticipate equipment failures, optimize asset performance, reduce downtime, and extend the lifecycle of renewable energy systems such as wind turbines, solar farms, and energy storage facilities.
This course is available in the following formats:
Virtual
Classroom
Request this course in a different delivery format.
Course Outcomes
Delegates will gain the knowledge and skills to:
Understand the fundamentals of AI-powered predictive maintenance and its relevance to renewable energy assets.
Analyze sensor and operational data to identify early warning signs of equipment degradation.
Apply AI models to predict potential failures and optimize maintenance schedules.
Assess cost-benefit impacts of predictive maintenance compared to traditional approaches.
Integrate predictive analytics into asset management strategies for improved reliability and sustainability.
At the end of this course, you’ll understand:
The course is designed for renewable energy professionals, asset managers, maintenance engineers, operations managers, data analysts, and decision makers in the energy sector who are keen to leverage AI-driven solutions for asset performance optimization. It is also valuable for technology consultants, policymakers, and researchers seeking to understand the role of predictive maintenance in the broader context of sustainable energy management.
✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
