🔍 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

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

🧠 Takeaways

This capstone was a powerful fusion of technical strategy and sustainable impact. It strengthened my leadership in enterprise architecture, data governance, and system optimization—key skills I now bring to renewable energy innovation.

📄 See the Full Project

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