PowerSense

Inspiration

As climate change accelerates, many young learners and households struggle to understand how energy consumption impacts the environment. We noticed that rooftop solar panels are installed widely, yet most users lack awareness of their actual energy generation and carbon savings. This inspired us to create PowerSense: an AI-powered platform that not only monitors solar panel health but also teaches learners how their energy decisions affect the planet.

What it does

Monitors panel-wise solar energy generation in real-time (Voltage, Current, Temperature, Irradiance, Power). Uses AI predictions (LSTM/Prophet) to forecast next 24 hours of energy generation. Shows overall grid supply-demand balance, teaching users how to optimize energy usage. Provides alerts and recommendations: e.g., shift appliance usage to peak solar hours to save energy and reduce carbon footprint. Gamifies learning: learners see real-time environmental impact of their choices, enhancing climate literacy.

How we built it

Frontend: React.js + TailwindCSS for responsive, interactive dashboards. Backend: FastAPI (Python) managing panel data ingestion, prediction calculations, and user management. Database: MongoDB stores panel readings and historical data. Real-time Updates: WebSockets for live sensor simulations and auto-updating graphs. ML Model: LSTM predicts future panel output based on historical data + weather API inputs (irradiance, temperature). Visuals: Recharts/Chart.js for live graphs, color-coded health cards, and grid visualization. We used simulated IoT data for live demonstrations and integrated OpenWeatherMap API for expected power generation.

Challenges we ran into

Data simulation: Generating realistic solar panel readings that reflect real-world variations. Prediction accuracy: Training the ML model to handle missing or noisy data from simulated sensors. Real-time visualization: Ensuring graphs update smoothly without affecting dashboard performance. Educational engagement: Designing interactive recommendations that are both fun and informative.

Accomplishments that we're proud of

Built a full-stack platform combining IoT simulation, AI prediction, and interactive visualization. Enabled learners to see the direct environmental impact of energy usage decisions. Created a system that scales from a single home to multiple sites, demonstrating global applicability. Successfully gamified learning with recommendations and alerts that teach sustainable energy habits.

What we learned

Hands-on experience with real-time IoT data ingestion and visualization. Integrating ML predictions into live dashboards for educational purposes. Importance of designing UI/UX for learners: clarity and engagement over complexity. How AI + education can be leveraged to raise awareness about climate action.

What's next for PowerSense

Expand to Mobile Platforms: Develop mobile applications to make PowerSense accessible to learners and communities in remote areas. Support Multiple Languages: Incorporate multilingual support to reach underserved populations and ensure inclusive climate education. Integrate with Real Solar Networks: Connect with live IoT-enabled solar panel systems for practical, real-world deployment and data collection. Gamify Sustainability: Introduce carbon footprint scoring, leaderboards, and achievement systems to engage users and encourage sustainable energy habits. Collaborate with Educational Programs: Partner with schools and climate education initiatives to provide interactive, hands-on learning experiences about renewable energy and sustainability.

Built With

• api
• css
• csv
• express.js
• fastapi
• firebase
• iot
• javascript
• lstm
• mongodb
• node.js
• openweathermap
• pdf
• prophet
• python
• react.js
• recharts
• simulated
• socket.io
• tailwind
• websockets