⚡ AI-Powered Energy Consumption Forecasting System

Predicting future electricity demand using Neural Networks and Time-Series Analysis to power the Smart Cities of tomorrow.

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🧠 Project Overview

This project is an end-to-end AI system designed to forecast energy consumption patterns. By leveraging MLP Regressors (Neural Networks) and sophisticated Time-Series Feature Engineering, the system provides actionable insights for power grid optimization and sustainable energy management.

💡 The Solution

• Predictive Modeling: Anticipates peak loads to prevent grid failure.
• Cost Efficiency: Helps industries optimize operational hours based on energy forecasts.
• Sustainability: Facilitates better integration of renewable energy sources by understanding demand curves.

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🛠️ Tech Stack

Category	Tools
Core AI	Python, Scikit-Learn (MLP Regressor), NumPy
Data Processing	Pandas, Time-Series Lagging, Rolling Statistics
Visualization	Matplotlib, Seaborn
Deployment	Streamlit (Interactive Dashboard), Flask (Rest API)

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🏗️ System Architecture

graph LR
    A[Raw Data] --> B[Feature Engineering]
    B --> C[MLP Neural Network]
    C --> D[Forecasting Engine]
    D --> E[Streamlit Dashboard]
    D --> F[Rest API]

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🚀 Key Features🔮

Advanced Forecasting: Uses Lag Features ($Lag_1$, $Lag_{24}$) to capture hourly and daily seasonality. 📊 Interactive Dashboard: Real-time "What-If" analysis for energy managers. 🚨 Anomaly Detection: Identifies unusual consumption spikes (potential leaks or equipment failure). 🧠 Smart Insights: Automated detection of peak usage hours and statistical trends. 📈 Visual InsightsEnergy Forecasting TrendAnomaly DetectionActual vs Predicted Energy (Neural Network)Identifying consumption spikes

📸 Dashboard & Insights Preview

⚡ Real-Time Demand Forecasting

Interactive Streamlit interface allowing users to adjust parameters and view instant AI-generated energy predictions.

🚨 Intelligence Anomaly Detection

Automated identification of consumption irregularities, highlighting potential energy leaks or equipment malfunctions.

🧠 Smart Usage Analytics

Deep-dive analytics showing peak consumption hours, weekend vs. weekday trends, and model confidence intervals.

📉 Accuracy Validation

Comparison of Ground Truth vs. MLP Neural Network predictions, demonstrating the model's ability to capture complex non-linear patterns.

🖥️ Dashboard Preview

Interactive Streamlit Interface for Real-Time Prediction

📂 Project StructurePlaintextAI-Energy-Forecasting/

├── data/               # Synthetic & Real-world datasets
├── models/             # Serialized (.pkl) ML models
├── outputs/            # Generated analytical plots
├── src/                # Modular Source Code
│   ├── preprocess.py   # Data cleaning logic
│   ├── feature_eng.py  # Lag & Rolling features
│   └── model.py        # Neural Network architecture
├── main.py             # Training & Evaluation pipeline
└── app.py              # Streamlit Web Application

⚙️ Installation & Usage

1️⃣ Setup EnvironmentBashgit clone cd AI-Energy-Forecasting python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt

2️⃣ Run the PipelineBash# Generate data and train the model python main.py

Launch the interactive dashboard

streamlit run app.py

👩‍💻 About the Developer

Shravani Mane CSE-AIML Student

I build AI solutions that bridge the gap between complex data and real-world impact. Developed with ❤️ for Sustainable Engineering.