Overcoming the False Promise of AI in Clean Energy Operations

Without trustworthy data, AI in clean energy fails to deliver on its promise of efficiency and optimization. AI is everywhere in clean energy. Has it delivered on its promise make operations more efficient? Too often, the hype outruns reality. Without a foundation of trustworthy data, even the most advanced algorithms fall short. In this talk, we’ll cut through the noise to expose what’s missing: data quality and trust. Fix that, and AI becomes the powerful tool it was meant to be for monitoring, analytics, and optimization.

Trusting your data starts with aligning “cloud truth” and “ground truth”—your performance data should match what’s happening in the real world. It sounds simple, yet renewable energy asset managers often struggle with data gaps, outdated insights, and unclear confidence levels. With only two asset managers and analysts per gigawatt and half of their time spent wrangling data instead of delivering strategic insights, the disconnect between AI’s promise and real-world operations is clear.

Through compelling stories and real-world examples, we’ll break down how standardization, transparency, and traceability in data platforms set the stage for more meaningful AI-driven insights. We’ll also explore how AI and machine learning enhance renewable energy operations, from analytics and optimization to streamlining workflows and improving user experience. Attendees will leave with a clear roadmap for making AI work, ensuring that when it raises an alert, it’s both actionable and reliable.