Smart Grid Optimizer & Peak Demand Predictor
A fuzzy logic–driven system that optimizes power distribution and forecasts peak electricity demand in real time.
View GitHub RepoProject Overview
This project implements a fuzzy logic–based Energy Management Controller (EMS) designed for smart grids. It automatically optimizes renewable resource integration, battery storage, and load forecasting to reduce peak electricity demand and improve grid stability.
Core Features
Fuzzy-Logic EMS
Expert-rule-based controller handles uncertainties in renewables and demand.
Peak Demand Forecasting
Short‑term load predictions prevent grid overload and reduce costs.
Renewables Integration
Optimizes energy usage across solar, wind, battery resources.
Improved Efficiency & Stability
Demonstrated ~20–22% boost in efficiency and reduced frequency variations.
Development Insights
The EMS uses a fuzzy inference engine to manage dynamic inputs (renewables, storage SoC, demand). Tuning rule sets and membership functions was key in balancing prediction accuracy and control response while smoothing peak loads.
Future Enhancements
- 📈 Integrate ML (e.g., RF / LSTM) with fuzzy architecture for better forecasting.
- 🌐 Real-world deployment in microgrid using embedded controllers.
- 🔋 Add integrated demand response and pricing-aware decision support.
- 📊 Dashboard visualizing load, SoC, and forecasted demand.