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Smart Home Energy Monitoring and Bill Forecasting
Subject area: Science,Engineering and Technology · Area of research: Smart Energy Monitoring Systems
DOI: 10.64388/IREV9I12-1719234
Abstract
The Smart Home Energy Monitoring and Bill Forecasting System is an IoT-based intelligent energy management solution designed to monitor household power consumption in real time and predict future electricity bills using Machine Learning techniques. The system integrates IoT simulation, Fire base cloud database, web technologies, mobile application development (MAD), R programming, and machine learning algorithms to create a complete smart energy ecosystem. The IoT module continuously simulates voltage, current, power, and energy consumption data using Python. The generated sensor data is transmitted to Firebase Real time Database for cloud storage and live synchronization. Machine learning models are integrated to analyze energy usage patterns and forecast future electricity bills based on consumption history. A web dashboard and mobile application provide users with live monitoring, energy analytics, alerts, and forecast reports. The project also demonstrates practical implementation of real-time cloud communication, smart energy analytics, predictive systems, and scalable web integration. The proposed system helps users reduce unnecessary energy usage, identify abnormal power spikes, and improve energy efficiency in smart homes.
Keywords
IoT, Smart Home, Energy Monitoring, Bill Forecasting, Machine Learning, Firebase, Web Dashboard, Mobile Application, R Programming
References
[1] Fire base Documentation–https://firebase.google.com
[2] Python Official Documentation–https://docs.python.org
[3] Scikit-Learn Documentation–https://scikit-learn.org
[4] MDN Web Docs–https://developer.mozilla.org
[5] W3Schools–https://www.w3schools.com
[6] R Programming Documentation–https://www.r-project.org
[7] IoT Research Paperson Smart Energy Monitoring Systems Machine Learning Techniques for Energy Forecasting Research Papers
[8] Dr. J. Narendra Babu "SMART WASTE IMAGE DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no. e469-e472, December-2025. URL: http://www.jetir.org/papers/JETIR2512457.pdf
[9] Dr. J. Narendra Babu, et.al, "Traffic Violation Fine Tracker", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.c608-c611, December-2025, URL: http://www.jetir.org/papers/JETIR2512268.pdf
[10] Dr. J. Narendra Babu, et.al, Journal of Internet Services and information security, AI-Enabled Forecasting and Isolation Forest-Based Detection of CBF Flow Anomalies in Secure Internet Architectures, Year 2025, Volume: 15, number: 3 (
[11] J. Narendra Babu, et.al– Indian License Plate Recognition System Based on Fuzzy Theory and BP Neural Network, IJECT Vol. 4, Issue 1, Jan - March 2013, ISSN: 2230-7109 (Online) | ISSN: 2230-9543 (Print)
How to cite this paper
@article{1719234,
author = {Dr. J. Narendra Babu, Meghana Sambare, Sahana Sarawad, Ramyashree S; Ranjitha M. G, Sangeetha M ; Sandhya H},
title = {Smart Home Energy Monitoring and Bill Forecasting},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {3080-3083},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719234.pdf},
abstract = {The Smart Home Energy Monitoring and Bill Forecasting System is an IoT-based intelligent energy management solution designed to monitor household power consumption in real time and predict future electricity bills using Machine Learning techniques. The system integrates IoT simulation, Fire base cloud database, web technologies, mobile application development (MAD), R programming, and machine learning algorithms to create a complete smart energy ecosystem. The IoT module continuously simulates voltage, current, power, and energy consumption data using Python. The generated sensor data is transmitted to Firebase Real time Database for cloud storage and live synchronization. Machine learning models are integrated to analyze energy usage patterns and forecast future electricity bills based on consumption history. A web dashboard and mobile application provide users with live monitoring, energy analytics, alerts, and forecast reports. The project also demonstrates practical implementation of real-time cloud communication, smart energy analytics, predictive systems, and scalable web integration. The proposed system helps users reduce unnecessary energy usage, identify abnormal power spikes, and improve energy efficiency in smart homes.},
keywords = {IoT, Smart Home, Energy Monitoring, Bill Forecasting, Machine Learning, Firebase, Web Dashboard, Mobile Application, R Programming},
month = {June},
doi = {https://doi.org/10.64388/IREV9I12-1719234}
}