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ui of the app
Due to high usage of power and high electricity bill, therefore we have decided to built this project
In this app , it can predict the next month power usage in watt ,next month electricity bill , it gives device alerts and ,multi-support device , show live monitoring of the electrical appliances and supports multiple devices.
To predict the next month power usage and the electricity bill we have a used machine learning first, i used the dummy dataset for the fan and then I preprocess data to check if there is any null value and it based on time series so I trained 2.5 months dataset using linear regression and random forest regressor and used 15 days for testing after training this I used the matplot of python library and then also used the jotlib from library to store it .After this I used flask to make it in into backend
Challenges that we ran into was to train the dataset of many electrical appliances it was time bounded to train many dataset of electrical appliances for the app
Accomplishments that we're proud of power saving that it can help in saving powers and also predict the next month power usage and electricity bill , It can live monitor the multiple devices and give alerts
What we learned for this project was machine learning and their algorithms and flask and I get to know how to integrate machine learning in a web app using flask
What's next for SMART MANAGER APP
Add More Appliances
Currently, the system monitors a fan and washing machine. Future updates will expand support to other household appliances like:
-Television
-Refrigerator
-Air conditioner
-Lights and chargers
Bill Estimation Dashboard
Integrate an analytics dashboard that:
Predicts monthly electricity bills
Shows trends in energy usage
Helps users make informed decisions to reduce consumption
Mobile App Integration
Build a dedicated mobile app (React Native or Flutter) so users can:
Monitor power consumption on-the-go
Get real-time alerts
Receive overuse warnings and usage tips
Smart Alerts & Automation
Add features to:
Notify users if a device is on but not being used
Suggest optimal times to run high-power appliances
Automatically turn off idle devices (using IoT control)
Advanced ML Models
Improve prediction accuracy using:
Time series forecasting (like ARIMA or LSTM)
Deep learning models for more complex usage patterns
Green Impact Metrics
Help users understand their carbon footprint and offer tips to reduce it.
Cloud Integration & Multi-user Support
Host the app on cloud servers with secure login and personalized dashboards for multiple users.
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