AI-Based Smart Power Consumption Prediction System

Abstract: This paper presents a uniquely designed Smart Energy Monitoring and Control System that integrates Internet of

Things (IoT), Artificial Intelligence (AI), and GSM communication to create an energy-efficient and predictive home

automation solution. The system continuously monitors household electrical parameters using ACS712 and ZMPT101B sensors

and transmits data via an Arduino Uno. A standout feature is its offline-capable design, where a GSM module (SIM900A) allows

users to control appliances and receive alerts through SMS without the need for internet. In addition, a powerful Long ShortTerm Memory (LSTM) neural network model is implemented to forecast future energy consumption using real-time data stored

in a local MySQL database. The system includes a fully functional website with user login, real-time monitoring dashboard,

prediction graphs, and a feedback module, providing an interactive user experience. This modular, low-cost, and intelligent

system is specially developed for regions with limited infrastructure and has demonstrated excellent performance in real-time

conditions.