EcoMeter
Empowering smarter energy use, one watt at a time.
Created on 20th August 2024
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EcoMeter
Empowering smarter energy use, one watt at a time.
The problem EcoMeter solves
Our AI-powered tool provides actionable insights that empower users to make smarter energy choices. Here's how it helps:
Real-Time Awareness: See energy usage live, so users know exactly when and how much energy they’re consuming at any given time.
Predictive Insights: Our AI model analyzes historical usage to forecast future energy needs and costs, allowing users to anticipate and adapt before they overspend.
Proactive Alerts: Instantly alerts users when their consumption exceeds set limits, enabling immediate adjustments to avoid surprises in their bills.
Personalized Energy-Saving Tips: Delivers tailored recommendations that are easy to implement, reducing waste without disrupting daily routines.
Impact
With our tool, users can confidently take control of their energy use, save on expenses, and contribute to a sustainable future by reducing unnecessary consumption.
Challenges we ran into
One major challenge we faced was establishing a reliable WebSocket connection to simulate real-time energy data. WebSockets are critical for streaming continuous data, but they can be tricky to manage when connections drop or lag occurs due to network instability.
The Hurdle
We encountered issues where the WebSocket connection would unexpectedly disconnect or fail to push data consistently, especially under simulated peak loads. This created gaps in our real-time data stream, which affected the accuracy and continuity of the analytics and predictions.
Our Solution
To address this, we implemented several strategies:
Automatic Reconnection Logic: We added an automatic reconnection mechanism that detects connection drops and immediately attempts to re-establish the WebSocket connection without interrupting the user experience.
Error Handling and Logging: We added detailed error handling and logging to capture any connection issues, allowing us to analyze and optimize performance in specific scenarios.
Data Buffering: To prevent data loss during reconnection, we added a small buffer to cache incoming data locally. Once reconnected, this buffer fills in any missed data points, ensuring a smooth user experience and accurate analytics.
By using these strategies, we were able to maintain a stable WebSocket connection that supports real-time data streaming for our users, making the experience seamless and consistent.
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