Electrify: Real-Time Analysis of Electricity Consumption and Bill Prediction


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.

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