EcoPulse

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Inspiration

Modern smart grids and microgrids face immense pressure due to unpredictable load spikes, renewable energy fluctuations, and inefficient energy management. We created EcoPulse to solve this challenge—providing an intelligent, real-time IoT dashboard that predicts energy surges and automates load shedding to ensure grid stability and reduce carbon footprint.

What it does

EcoPulse is a smart energy monitoring and forecasting system. It connects to virtual IoT telemetry streams (tracking current load in kW, ambient temperature, and CO2 levels) and applies an AI predictive algorithm to forecast future consumption.

Key features:

• Live Telemetry Streams: Tracks real-time grid metrics with instantaneous updates.
• AI Load Forecasting: Predicts upcoming load spikes before they overload the grid.
• Automated AI Shedding: Automatically turns off low-priority heavy consumers (like EV Chargers or HVAC systems) when grid overload thresholds are detected.
• Interactive IoT Control Panel: Allows manual override and monitoring of individual connected hardware devices based on priority tags.

How we built it

• Frontend & UI: Next.js (App Router), React, Tailwind CSS.
• Data Visualization: Recharts for smooth real-time telemetry charts.
• IoT & AI Engine: Custom Javascript predictive load algorithms simulating real-time telemetry streaming and automated peak-shaving logic.
• Icons & Styling: Lucide React icons with a modern, dark-themed dashboard UI.
• Deployment: Vercel for continuous integration and hosting.

Challenges we ran into

Integrating real-time dynamic charts with fluctuating telemetry state while ensuring smooth automated trigger responses during peak loads required careful React state management. Additionally, handling nested directory structures during Vercel CI/CD deployment was a great learning milestone for Git workflows.

Accomplishments that we're proud of

• Successfully created a highly responsive, modern IoT-focused UI with live data streams.
• Implemented automated peak-shaving logic that visually demonstrates how AI can balance energy grids proactively.

What we learned

• Mastered Git/GitHub collaboration workflows, including remote history synchronization and sub-directory root configurations on Vercel.
• Deepened understanding of building reactive telemetry dashboards with Next.js and Tailwind CSS.

What's next for EcoPulse

• Integrating real hardware microcontrollers (ESP32/Arduino) via WebSockets/MQTT.
• Implementing advanced machine learning models (TensorFlow.js) trained on historical grid consumption data for hyper-accurate demand prediction. ## What we learned

What's next for EcoPulse

Built With

• ai
• git
• github
• iot
• next.js
• react
• recharts
• tailwindcss
• typescript
• vercel