Synergy
Smart, Not just Automated: Rethinking energy efficiency at home.
Created on 18th April 2025
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Synergy
Smart, Not just Automated: Rethinking energy efficiency at home.
The problem Synergy solves
1.Overview: Synergy is a next-gen smart home system designed to bridge the gap between automation and true intelligence. While traditional smart homes respond to triggers, Synergy thinks ahead, learns from behavior, and optimizes energy usage in real time to make homes not just more convenient — but significantly more energy-efficient and sustainable.
2.Problem Statement: Everyday energy inefficiencies — like lights left on, idle appliances running, or erratic consumption patterns — silently contribute to rising electricity bills and environmental strain. Most current smart home solutions only automate, without learning or adapting.
3.Our Solution: Synergy combines IoT and Machine Learning to build a real-time, adaptive energy optimization system. By continuously learning from device usage, room occupancy, and user behavior, it can predict, detect anomalies, and recommend actions — even taking them automatically when configured.
4.Impact: Synergy empowers users to reduce their carbon footprint, lower bills, and shift from reactive to proactive energy management — bringing us closer to a sustainable, intelligent future.
How will it be able to solve the problem?
1.Tracks Energy in Real-Time: IoT sensors monitor appliances, rooms, and user presence.
2.Predicts Usage: ML models forecast power consumption to prevent waste.
3.Detects Anomalies: Spots unusual behavior (like devices left on) and acts automatically.
4.Optimizes Load: Identifies peak times and suggests smarter energy distribution.
5.Learns User Behavior: Adapts to routines for long-term energy savings.
6.Natural Control: Accepts simple voice/text commands for effortless interaction
Challenges we ran into
1.Database Configuration & Deployment
One of our initial hurdle was a workable real data of energy consumption — most datasets were either incomplete not in a compatible format. More over that, deploying the backend database using platforms like MySQL or PostgreSQL was tricky, especially when dealing with remote hosting configurations and access permissions. Conversion between the code of PostgreSQL ad MySQL was also a not so easy task. By experimenting with services like Railway, PlanetScale, and others, we finally established a secure, cloud-hosted database — highlighting our persistence and ability to navigate platform limitations.
2.Frontend-Backend Integration
Integrating the frontend UI with backend logic wasn’t that easy of a task. We ran into issues with various endpoint mismatches and inconsistent data rendering. By debugging API calls using tools like Postman and thouroughly testing JSON responses, we streamlined the data flow. This not only improved performance but also showed our commitment to delivering a seamless user experience across components.
3.IoT Sensor Data Format Handling
The data from IoT sensors varied in format, and integrating it directly into our system would have led to inaccurate logging and analytics. We tackled this by designing a normalization pipeline to standardize incoming data in real-time. This process required deep attention to structure, units, and consistency — showcasing our strength in data engineering and real-time processing.
