Data to Dollars: An AI Approach for Automated Wind Portfolio Management
While AI-powered normality models are increasingly common for detecting anomalies in wind turbines, they often provide an incomplete picture for decision-making. (Presentation in English)
True, actionable intelligence requires moving beyond simple deviation alerts to understand the deeper operational context—which specific components are affected—and, most critically, the financial impact of an issue. This presentation showcases an advanced AI platform that completes the data-to-value cycle. We will demonstrate a fully automated approach that starts with unlabelled SCADA data and progresses not only to contextualized diagnostics but also to the calculation of financial KPIs, such as revenue loss from downtime and projected O&M costs. By translating complex technical events into clear financial terms, we empower portfolio managers and maintenance teams to prioritize interventions effectively, ensuring that decisions are driven by the most critical metric: the bottom line.
Speakers (1)
