EcoSmart-Campus Optimize Campus Energy Consumption with Mathematical Intelligence Our advanced system uses Discrete Mathematics models to predict, monitor, and optimize electricity usage across your campus, reducing costs and environmental impact.
Each mathematical model (probability, recurrence, Boolean logic, graph theory) is mapped to its own interactive tool for modular scalability.
🧩 Features at a Glance ✅ Real-time energy monitoring ✅ Graph-based campus layout builder ✅ Smart scheduling and occupancy prediction ✅ Automated device control ✅ Bayesian probability simulations ✅ Minimum Spanning Tree (MST) optimization ✅ Visual logic editor with truth tables ✅ Customizable settings and API integrations ✅ Comprehensive reports and exports ✅ Admin-level analytics and performance insights
🧠 Mathematical Engine Energy Function: f(r,t) → Real-time energy consumption of room r at time t
Savings Recurrence: S_n = E_{n-1} - E_n → Day-over-day energy improvement
Occupancy Probability: P_r(t) = Hours Occupied / Total Hours
Graph Optimization: MST via Prim’s / Kruskal’s Dominating Set → Optimal sensor placement
Automation Logic: L_r(t) = O_r(t) ∧ C_r(t) → Combined room and condition rule
🧭 Navigation Flow Homepage → Dashboard → Campus Layout → Devices → Analytics → Tools → Settings/Admin
🧩 Future Enhancements AI-based anomaly detection Dynamic campus graph scaling Cloud sync & offline mode Voice-based device control Predictive maintenance alerts 💚 Impact This project not only reduces electricity waste but also builds a foundation for sustainable, AI-powered campuses. It merges machine learning, optimization, and real-time systems into a single dashboard — bridging theoretical math with real-world utility.
👨💻 Developer Neev Paraskumar Modh 🎓 2nd Year B.Tech in AIML, Nirma University 💡 Passionate about Data Science, ML, and Systems Optimization 📧 [Contact via GitHub Issues or Email Placeholder]
🏷️ License This project is released under the MIT License – free for learning and research use.
⚙️ “Smart campuses aren’t built overnight — they’re modeled, optimized, and evolved through data.”
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