Authors: Assistant Professor Dr. K. N. Kazi, Bandgar Pooja Kisan, Chavan Sahil Sanjay, Mali Nikhil Vikas
Abstract: Rising energy consumption and increasing electricity costs have created a need for intelligent energy management systems. This paper presents an AI-Powered Voice-Controlled Energy Tracking and Bill Prediction System developed using Java Full Stack technology and Machine Learning techniques. The proposed system enables users to monitor real-time energy consumption, predict future electricity bills, and interact with the system through voice commands. Historical energy usage data is analyzed using machine learning algorithms to forecast future consumption patterns and billing amounts with improved accuracy. The voice-controlled interface enhances user convenience and accessibility by allowing hands-free operation and quick access to energy-related information. The system integrates a responsive web application, database management, and predictive analytics to provide a comprehensive energy monitoring solution. Experimental results demonstrate that the proposed model effectively tracks energy usage, generates accurate bill predictions, and promotes energy-saving behavior among consumers. This solution contributes to the development of smart energy management systems and supports efficient utilization of electrical resources in residential and commercial environments.
DOI: http://doi.org/
