Smart Energy Consumption Forecasting and Optimization using TPE-Optimized XGBoost

An AI-powered smart energy forecasting and optimization system that predicts appliance energy consumption using a TPE-optimized XGBoost regression model and provides intelligent energy-saving recommendations through an interactive Streamlit dashboard.

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Overview

This project focuses on predicting appliance energy consumption in smart homes using advanced machine learning techniques inspired by modern energy forecasting research.

The system combines:

• Feature Engineering
• Bayesian Hyperparameter Optimization
• XGBoost Regression
• Cross Validation
• Interactive Dashboarding

The project is inspired by research on:

Bayesian-based optimized XGBoost algorithms for residential energy usage prediction.

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Key Features

• Smart energy consumption forecasting
• TPE-based Bayesian hyperparameter optimization
• Advanced feature engineering
• 10-Fold Cross Validation
• Peak-hour detection
• Energy optimization suggestions
• Interactive Streamlit dashboard
• Feature importance visualization
• Real-time prediction simulation

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Dataset

Dataset Used:

• UCI Appliances Energy Prediction Dataset
  https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction

Dataset Contains

• Appliance energy consumption
• Indoor temperature
• Humidity measurements
• Environmental conditions
• Time-based information

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Technologies Used

Category	Technology
Language	Python
Machine Learning	XGBoost
Hyperparameter Optimization	Hyperopt (TPE)
Data Processing	Pandas, NumPy
Visualization	Matplotlib
Dashboard	Streamlit
Evaluation	Scikit-learn

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Project Workflow

Raw Dataset
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Data Preprocessing
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Feature Engineering
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TPE Hyperparameter Optimization
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Optimized XGBoost Training
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10-Fold Cross Validation
     ↓
Performance Evaluation
     ↓
Prediction & Optimization
     ↓
Interactive Dashboard