Authors :
Abhijith C; Amanyujith Raj; Aiswariya R; Fathimath Hanna Safeer; Nikhil Dharman M K
Volume/Issue :
Volume 8 - 2023, Issue 3 - March
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/42YwbEI
DOI :
https://doi.org/10.5281/zenodo.7793093
Abstract :
In this scenario, it is essential to optimize
power usage and promote efficient energy consumption.
The increasing electricity demand has put a strain on
natural resources and the environment. To address this,
there is a need to optimize energy usage and reduce waste.
The proposed system aims to help users achieve this by
providing real-time analysis of electricity consumption
and predicting monthly electricity bills using machine
learning algorithms. The system employs a current sensor
to track the energy consumed by each device in a house
and estimates the monthly electricity bill. The user can set
a monthly desired bill, and as the energy consumption
approaches the set limit, the user is notified through the
software application. The LSTM algorithm is used for
predicting the monthly electricity bill based on the data
collected from the current sensor. The algorithm takes
into account various factors such as the energy consumed
by different devices, time of day, and historical data to
provide accurate predictions. Firebase is used as a cloud
service for storing and processing data. It allows for
efficient and secure storage of data and provides real-time
updates, ensuring that users always have access to the
latest information. The proposed system offers numerous
benefits, including improved energy efficiency and cost
savings.
In this scenario, it is essential to optimize
power usage and promote efficient energy consumption.
The increasing electricity demand has put a strain on
natural resources and the environment. To address this,
there is a need to optimize energy usage and reduce waste.
The proposed system aims to help users achieve this by
providing real-time analysis of electricity consumption
and predicting monthly electricity bills using machine
learning algorithms. The system employs a current sensor
to track the energy consumed by each device in a house
and estimates the monthly electricity bill. The user can set
a monthly desired bill, and as the energy consumption
approaches the set limit, the user is notified through the
software application. The LSTM algorithm is used for
predicting the monthly electricity bill based on the data
collected from the current sensor. The algorithm takes
into account various factors such as the energy consumed
by different devices, time of day, and historical data to
provide accurate predictions. Firebase is used as a cloud
service for storing and processing data. It allows for
efficient and secure storage of data and provides real-time
updates, ensuring that users always have access to the
latest information. The proposed system offers numerous
benefits, including improved energy efficiency and cost
savings.
