Renewable Energy Predictive Maintenance System (RE-PMS)

Inspiration

A Vision for Smarter Renewable Energy

Growing up in Vietnam, we have been inspired by the country's rapid development and commitment to renewable energy. Bình Thuận Province, with its vast wind farms and strong coastal winds, stands at the forefront of Vietnam’s clean energy revolution. However, challenges such as turbine failures, high maintenance costs, and unpredictable weather conditions threaten efficiency and progress.

To address this, we developed an AI-powered prediction model designed to prevent turbine failures before they occur. But as we researched further, we realized that these challenges aren’t just limited to wind energy. Solar farms, hydroelectric plants, and other renewable energy infrastructures all face similar maintenance hurdles.

This inspired us to create a scalable, data-driven predictive maintenance system that enhances efficiency, reduces downtime, and optimizes maintenance costs across multiple renewable energy sectors.

What it does

Our system analyzes real-time operational data from renewable energy infrastructure to predict potential failures before they occur. By leveraging machine learning models, it identifies patterns and anomalies in key performance indicators, enabling proactive maintenance and improved efficiency. When the system predicts a failure, the model automatically

📩 Send an email alert to engineers and maintenance teams, detailing:

The number of machines at risk
The specific machine at risk

📊 Updates a live dashboard, where users can:

View affected machines
Download reports for maintenance planning

By providing early warnings, our solution helps operators:
✅ Reduce unexpected breakdowns
✅ Optimize maintenance scheduling
✅ Extend the lifespan of equipment
✅ Improve energy efficiency

How we built it

To create our system, we:
✅ Collected real-world sensor data from renewable energy systems.
✅ Trained a machine learning model (Random Forest Classifier) to predict failures based on operational data.
✅ Integrated automated email notifications, allowing engineers to respond before critical failures.
✅ Built a visualization dashboard using Streamlit. allowing engineers & operators to view predictions and take immediate action.

Challenges we ran into

🔴 Data Sourcing Issues – Finding useable datasets was time-consuming.
🔴 Reducing False Alarms – Our early models flagged too many false positives, requiring fine-tuning.
🔴 Secure Email Alerts – Implementing a reliable notification system with proper authentication took multiple iterations.

Accomplishments that we're proud of

✅ Successfully Developed an AI Model – We trained a machine learning model that accurately predicts failures before they happen, helping to reduce downtime and maintenance costs.
✅ Scalability Across Renewable Energy Systems – While initially designed for wind turbines, our model is adaptable to solar farms, hydroelectric plants, and other clean energy sources, making it a versatile solution.
✅ Overcoming Technical Challenges – From data preprocessing to integrating a reliable alert system, we tackled various obstacles and built a fully functional prototype.

What we learned

Through research, we found that machine learning can analyze sensor data to predict failures before they occur. Many industries already use predictive analytics for maintenance, but renewable energy systems lacked accessible, AI-powered solutions.

What's next for Renewable Energy Predictive Maintenance System (RE-PMS)

🔍 Improving Prediction Accuracy

Fine-tune our machine learning models with more diverse and high-quality datasets.
Explore deep learning techniques to improve failure detection in complex energy systems.

📡 Real-Time IoT Integration

Integrate IoT sensors for live monitoring of equipment health.
Implement edge computing to process data faster and reduce cloud dependency.

📊 Advanced Dashboard & Reporting