1. Abstract
The increasing demand for electricity and the rise in energy wastage have created the need for intelligent energy monitoring and optimization systems. The proposed project, “AI-Based Smart Energy Consumption Analyzer and Optimization System Using Machine Learning and Flask,” is designed to predict household energy consumption accurately and provide smart optimization suggestions using Artificial Intelligence and Machine Learning techniques.
The system uses the XGBoost Regressor algorithm to analyze smart home sensor data such as temperature, humidity, pressure, wind speed, and lighting conditions. Advanced feature engineering techniques including lag features, rolling statistics, interaction features, and temporal features are implemented to improve prediction accuracy. The trained model achieves approximately 90%–95% prediction accuracy.
A Flask-based web application is developed to provide a secure and user-friendly interface for users. The system also integrates the Groq API and Large Language Models to generate intelligent energy-saving recommendations. In addition, graphical visualizations such as energy trend graphs, actual vs predicted graphs, and correlation heatmaps are generated for better analysis.
The proposed system helps users reduce electricity wastage, estimate electricity bills, and improve energy efficiency through intelligent prediction and optimization.





2. Introduction
Electricity plays a major role in modern homes, industries, and smart city infrastructures. Due to increasing electricity consumption and rising utility costs, efficient energy management has become an important challenge. Traditional energy monitoring systems mainly provide historical usage information and do not support intelligent prediction or optimization.
Artificial Intelligence (AI) and Machine Learning (ML) technologies provide powerful solutions for smart energy management systems. By analyzing historical energy usage patterns and environmental conditions, machine learning models can predict future energy consumption accurately and help users optimize electricity usage.
The proposed system uses the XGBoost machine learning algorithm for predicting household appliance energy consumption. The project also integrates Flask web technology for creating an interactive web application and Groq AI API for generating intelligent optimization suggestions.
The system performs:
• Energy consumption prediction
• Electricity bill estimation
• AI-based optimization recommendations
• Graphical energy analysis
• Secure user authentication
The project demonstrates how AI and ML can be effectively applied to real-world smart energy management applications.

3. Objective
The main objectives of the proposed system are:
• To develop a machine learning model for predicting household energy consumption accurately.
• To implement advanced feature engineering techniques for improving prediction performance.
• To develop a secure Flask-based web application with user authentication.
• To generate AI-powered energy optimization suggestions using Groq API and LLMs.
• To visualize energy usage patterns through graphs and charts.
• To estimate electricity bills based on predicted energy consumption.
• To reduce electricity wastage and improve energy efficiency.

Block Diagram