AI for Energy Efficiency
Developed AI-driven optimization algorithms for smart grids reducing energy waste by 28% in pilot cities. Published in Nature Sustainability.
Project Overview
This project combines machine learning with smart grid infrastructure to optimize energy distribution. By analyzing real-time data from IoT sensors, our algorithm dynamically adjusts power flows, minimizing loss and enhancing grid stability.
Key Features
- Real-time energy flow optimization
- Predictive demand forecasting
- Smart grid monitoring dashboard
Our Approach
We used a hybrid reinforcement learning approach to train the algorithm on historical energy consumption patterns. The model was then integrated with real-time IoT data from 5 pilot cities to validate its performance in complex grid environments.
Reinforcement Learning
- • Trained on 5-year historical consumption data
- • Adapted to 300+ variable grid configurations
- • Achieved 97% accuracy in forecasting
Smart Grid Integration
- • API integration with 200+ grid sensors
- • Real-time data processing at 5ms latency
- • 99.9% system uptime
Results
28%
Reduction in energy waste
42%
Cost savings for grid operators
3.2M
CO₂ emissions averted annually
Timeline
2021
Algorithm development and pilot testing
2022
Deployment in 5 pilot cities
2023
Nature Sustainability publication
Technical Stack
Python
TensorFlow
Apache Kafka
PostgreSQL
All data and results available upon request for collaboration purposes