🔍 Project Overview

As part of my graduate capstone at Boston University, I led the design of an AI-driven Enterprise Architecture (EA) for Ørsted, one of the world’s leading renewable energy companies. This strategic roadmap leverages predictive AI, SCADA data, and ERP integration to reduce turbine downtime and boost operational efficiency.

⚡ The Challenge

Ørsted needed to move from reactive maintenance to proactive, data-driven decision-making. I aimed to deliver a system that improves real-time performance visibility, reduces costs, and maximizes output across their global wind portfolio.

💡 The Solution

I proposed an enterprise-wide AI architecture with:

• Predictive maintenance modules

• Integrated SCADA & ERP systems (SAP)

• Cloud-based data infrastructure (AWS/Azure)

• Dashboards with actionable insights

• TOGAF-aligned framework for long-term scalability

📈 Projected Results

• 15% reduction in turbine downtime

• 10% increase in energy output

• 20% improvement in maintenance efficiency

🛠️ Tools & Technologies

Microsoft Visio · Lucidchart · ArchiMate · Python · Tableau · SAP ERP · AWS & Azure · SCADA data

🧠 Takeaways

This project fused strategic thinking with real-world clean energy outcomes. It strengthened my leadership in enterprise architecture, system optimization, and AI adoption—skills I now bring to renewable energy innovation.

📄 See the Full Project

📘 Download Full Report (PDF)
📊 View Presentation Slides (PDF)