KALYAN1188/Household-Energy-Consumption-Predictor

🏠 Predicting Appliance Energy Consumption In Households

   

ML-powered household energy consumption prediction with comprehensive insights for energy optimization, cost reduction, and grid management.

🎯 Project Overview

Our client, an energy management company, aims to enhance their understanding of appliance energy consumption patterns in residential settings. This project leverages machine learning to predict appliance energy usage based on various environmental and household factors.

🌟 Project Benefits

📊 Model Performance

Interpretation: Model explains 70% of energy variance with average error of ±14.27 Wh. Negative overfit gap indicates excellent generalization — model performs better on unseen data!

🚀 Live Demo

Try the live application here: 🔗 [Hugging Face Space](https://huggingface.co/spaces/YOUR_USERNAME/energy-consumption-predictor)

📁 Project Structure

household-energy-prediction/
├── notebook.ipynb                    # Complete ML pipeline (12 steps)
├── app.py                            # Gradio deployment app
├── final_model.pkl                   # Trained model package
├── requirements.txt                  # Python dependencies
├── README.md                         # This file
├── energydata_complete.csv           # Dataset
└── outputs/                          # Generated visualizations
    ├── 01_target_analysis.png
    ├── 02_feature_distributions.png
    ├── 03_correlation_analysis.png
    ├── 04_time_patterns.png
    ├── 05_scatter_plots.png
    ├── 06_room_analysis.png
    ├── 07_lights_analysis.png
    ├── 08_weather_analysis.png
    ├── 09_multicollinearity.png
    ├── 10_consumption_categories.png
    ├── 11_outlier_handling.png
    ├── 12_feature_engineering.png
    ├── 13_model_comparison.png
    ├── 14_cross_validation.png
    ├── 15_feature_importance.png
    ├── 16_shap_importance.png
    ├── 17_shap_beeswarm.png
    └── 18_error_analysis.png

🛠️ Installation

Prerequisites

• —Python 3.11 or higher
• —pip package manager

Step 1: Clone Repository

git clone https://github.com/YOUR_USERNAME/household-energy-prediction.git
cd household-energy-prediction

Step 2: Create Virtual Environment (Recommended)

# Create virtual environment
python -m venv venv

# Activate — Windows:
venv\Scripts\activate

# Activate — Mac/Linux:
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

🎓 How to Reproduce

Option 1: Run Complete Training Pipeline

# Step 1: Open Jupyter notebook
jupyter notebook notebook.ipynb

# Step 2: Run all cells from top to bottom
# The notebook will automatically:
#   - Load and clean data
#   - Perform comprehensive EDA (11 sections)
#   - Engineer 40+ features including lag features
#   - Train 14 different models
#   - Perform hyperparameter tuning
#   - Run time series cross-validation
#   - Generate SHAP analysis
#   - Create 18 visualizations
#   - Save final model as final_model.pkl

# Step 3: Wait for completion (~15-20 minutes)
# Step 4: Check outputs/ folder for all generated plots

Option 2: Use Pre-trained Model

# Run Gradio app directly
python app.py

This launches the interface at http://localhost:7860

🔬 Methodology

1. Data Overview

• —Dataset: 19,735 records at 10-minute intervals (Jan–May 2016)
• —Target: Appliances — energy use in Wh
• —Features: 29 original → 40+ after engineering
• —Missing Values: None ✅
• —Duplicate Rows: None ✅

2. Data Split Strategy

TIME-BASED SPLIT (respects temporal order):
├── Train  : 70% → Jan–Mar 2016
├── Val    : 15% → Mar–Apr 2016
└── Test   : 15% → Apr–May 2016

✅ Split done BEFORE any preprocessing (zero data leakage)
✅ No shuffle — preserves time series order

3. Exploratory Data Analysis (11 Sections)

4. Feature Engineering (40+ Features)

Time Features:

• —Hour, day of week, month, day
• —Is weekend, is night, is peak hour
• —Minute of day

Temperature & Humidity Aggregates:

• —Average/max/min indoor temperature
• —Indoor temperature range
• —Indoor vs outdoor differential

Interaction Features:

• —Temperature × Humidity
• —Temperature × Hour
• —Lights × Hour

Lag Features (Most Important!):

• —lag1 to lag12 (10 min to 2 hours ago)
• —lag_144 (1 day ago)
• —lag_288 (2 days ago)

Rolling Window Features:

• —Rolling mean (3, 6, 12, 24 intervals)
• —Rolling std, max, min (6 intervals)

5. Outlier Handling

Strategy:
├── Train  : REMOVE outliers (IQR method, multiplier=1.5)
├── Val    : CAP outliers at bounds
└── Test   : CAP outliers at bounds

Bounds computed on TRAIN ONLY → applied to Val/Test

6. Model Training & Comparison

Evaluated 14 regression models:

Best Model: Lasso Regression

• —Val R²: 0.672 (highest among all models)
• —Overfit Gap: -0.047 (no overfitting!)

7. Hyperparameter Tuning

Method    : RandomizedSearchCV
CV        : 5-Fold Time Series Split
Iterations: 50
Scoring   : R²
Best Alpha: 0.1

8. Model Evaluation

A) Time Series Cross-Validation (5-Fold):
   R² Mean  : 0.6050 ± 0.0622
   MSE Mean : 305.41 ± 55.23
   RMSE Mean: 17.45 ± 1.60
   MAE Mean : 12.07 ± 0.87

B) SHAP Analysis:
   - LinearExplainer for interpretability
   - Lag features dominate importance
   - Rolling features second most important

C) Comprehensive Error Analysis:
   - 9-plot error visualization
   - Error by price range
   - Cumulative error distribution

9. Key Findings

1. 1.Lag features are the strongest predictors — what happened in the last 10-60 minutes best predicts current usage
2. 2.Linear models outperform tree models — lag features create linear relationships
3. 3.Peak hours (5-9PM) show highest energy consumption
4. 4.Energy is 57% higher when lights are ON vs lights off
5. 5.No overfitting — model generalizes perfectly to unseen data

10. Deployment

• —Framework: Gradio 5.10.0
• —Features:
• —5 output tabs (Prediction, Cost, Grid, Tips, Summary)
• —Real-time cost estimation in ₹
• —Peak hour warnings
• —Personalized energy saving tips
• —Grid management recommendations
• —CO₂ emissions tracking
• —Platform: Hugging Face Spaces

📊 Visualizations Generated

🐛 Troubleshooting

Model Loading Issues

import joblib, sklearn
print(f"sklearn version: {sklearn.__version__}")
# Should be 1.6.1

SHAP Installation Issues

# Windows
pip install shap --no-build-isolation

# Linux/Mac
pip install shap

Gradio Version Issues

pip install gradio==5.10.0

📈 Future Improvements

• —[ ] Add more data sources (smart meter data)
• —[ ] Implement occupancy detection features
• —[ ] Real-time data streaming
• —[ ] Mobile app deployment
• —[ ] API for third-party integration
• —[ ] Advanced deep learning models (LSTM)

🤝 Contributing

Contributions are welcome!

1. 1.Fork the repository
2. 2.Create a feature branch
3. 3.Make your changes
4. 4.Submit a pull request

📄 License

This project is licensed under the MIT License.

👨‍💻 Developer

Sai Kalyan Machine Learning Engineer

• —📧 Email: kalyan.kothwal@gmail.com
• —💼 LinkedIn: Your LinkedIn
• —🐙 GitHub: Your GitHub
• —🌐 Portfolio: Your Portfolio

📚 References

1. 1.UCI Machine Learning Repository — Appliances Energy Prediction Dataset
2. 2.Candanedo, L.M., Feldheim, V., Deramaix, D. (2017). "Data driven prediction models of energy use of appliances in a low-energy house"
3. 3.SHAP: Lundberg, S.M., & Lee, S.I. (2017). "A Unified Approach to Interpreting Model Predictions"
4. 4.Lasso Regression: Tibshirani, R. (1996). "Regression Shrinkage and Selection via the Lasso"

⭐ If you found this project helpful, please give it a star!

Last Updated: March 2026 | Version: 1.0