⚡ AI-Powered Energy Consumption Forecasting System

🚀 Project Overview

This project is a Machine Learning-based Energy Forecasting System that predicts future electricity consumption using historical data.

It is designed to simulate real-world industry use cases such as smart cities, energy optimization, and intelligent power management systems.

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🎯 Problem Statement

Energy consumption is increasing rapidly, leading to:

• ⚠️ High electricity costs
• ⚠️ Energy wastage
• ⚠️ Inefficient resource utilization

Traditional systems cannot predict future demand accurately.

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💡 Solution

This project uses Machine Learning (Random Forest Regressor) to:

✔ Analyze historical energy usage ✔ Learn consumption patterns ✔ Predict future energy demand ✔ Help optimize energy usage

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🏭 Industry Relevance

This system can be used in:

• 🏙 Smart Cities → Efficient power distribution
• ⚡ Electricity Boards → Load forecasting
• 🏭 Manufacturing → Reduce operational costs
• 🖥 Data Centers → Prevent power overload
• 🌱 Renewable Energy → Balance supply & demand

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🧠 Tech Stack

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib
• Model: Random Forest Regressor
• Visualization: Matplotlib
• Deployment: Streamlit

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🏗 Project Architecture

CSV Data → Data Cleaning → Feature Engineering → Model Training → Prediction → Visualization (Streamlit)

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📂 Project Structure

AI-Energy-Forecasting/
│
├── data/
├── src/
├── models/
├── outputs/
├── images/
├── notebooks/
├── app.py
├── main.py
├── requirements.txt
└── README.md

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⚙️ Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/YOUR_USERNAME/AI-Energy-Forecasting-System.git
cd AI-Energy-Forecasting-System

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Run Model Training

python main.py

4️⃣ Run Streamlit App

streamlit run app.py

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📊 Features

✔ Upload your own CSV dataset ✔ Automatic data preprocessing ✔ Feature engineering (time-based) ✔ ML model prediction ✔ Interactive dashboard ✔ Graph visualization (Actual vs Predicted) ✔ Download prediction results

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📸 Project Screenshots

🔹 Dashboard

🔹 Data Preview

🔹 Prediction Graph

🔹 Comparison Graph

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📈 Output Example

• 📊 Energy Consumption Trends
• 🔮 Predicted Energy Values
• 📉 Model Performance Visualization
• 📥 Downloadable Prediction CSV

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🧪 Model Performance

• 📉 RMSE (Root Mean Squared Error)
• 📊 R² Score (Accuracy Measure)

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🔮 Future Improvements

• 🔥 Deep Learning (LSTM for time-series forecasting)
• 📅 Future prediction (next 7 days/month)
• 🌐 Cloud deployment
• 📱 Mobile-friendly UI
• 🤖 AI-based energy saving recommendations

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🎓 Learning Outcomes

• Real-world ML pipeline implementation
• Data preprocessing & feature engineering
• Model training & evaluation
• Streamlit app deployment
• GitHub project structuring

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👨‍💻 Author

Ananya Pradhan B.Tech IT Student

🔗 LinkedIn: www.linkedin.com/in/ananya-pradhan-10bb462ba

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🌟 Why This Project Stands Out

✔ Real-world problem solving ✔ End-to-end ML pipeline ✔ Interactive dashboard ✔ Industry-relevant use case ✔ Clean and modular code

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⭐ If you like this project

Give it a ⭐ on GitHub and feel free to connect!

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