An end-to-end AI-powered energy intelligence system designed to forecast future energy consumption using machine learning, API engineering, and a modern responsive frontend.

The platform combines:

• 🧠 Machine Learning Forecasting
• 🌐 Flask API Backend
• 💻 Interactive Frontend UI
• ☁️ Cloud Deployment Architecture
• 📊 Feature Engineering Pipelines
• 📁 Structured AI Project Architecture

This system predicts next-hour energy usage (kWh) while simulating real-world smart energy forecasting workflows used in intelligent infrastructure and sustainability analytics systems.

Stars

Forks

Python

Flask

XGBoost

Testing

Status

License

Render

Netlify

Live Demo

⚡ AI-Powered Smart Energy Forecasting • Flask API • Full-Stack AI Engineering • Cloud Deployment

Modern energy systems require intelligent forecasting solutions to optimize energy usage, infrastructure efficiency, and operational planning.

Unexpected demand spikes can lead to:

• energy inefficiencies
• infrastructure overload
• operational cost escalation
• sustainability challenges
• poor energy utilization

This project builds a complete AI-powered forecasting platform capable of:

✅ Forecasting future energy consumption
✅ Providing intelligent energy insights
✅ Serving predictions through APIs
✅ Delivering responsive UI workflows
✅ Supporting smart energy analytics
✅ Simulating enterprise energy intelligence systems

This project is deployed using a modern full-stack AI deployment workflow.

This architecture simulates production-style AI deployment workflows commonly used in AI-powered web systems.

https://ai-energy-forecasting-system.netlify.app/

https://ai-energy-forecasting-system.onrender.com

This project simulates AI-powered smart energy forecasting workflows used in:

• smart grid analytics
• industrial energy monitoring
• utility consumption forecasting
• sustainability analytics
• operational energy optimization
• intelligent infrastructure systems

The platform combines machine learning forecasting, API engineering, and frontend systems to deliver real-time energy intelligence workflows.

Models implemented:

• XGBoost
• MLP Neural Network
• LSTM Neural Network

Capabilities:

• next-hour energy forecasting
• feature-engineered predictions
• intelligent energy analytics
• comparative ML experimentation

The best-performing model is automatically saved as:

best_model.pkl

REST API backend for serving energy forecasts.

• /api/predict endpoint
• timestamp handling
• JSON response support
• model inference pipeline
• backend validation handling

Modern responsive frontend built using:

• HTML
• CSS
• JavaScript

• dark/light mode
• centered dashboard layout
• prediction visualization
• smooth interactions
• responsive design
• prediction history

The application supports:

• prediction history storage
• CSV export
• session tracking
• history clearing

This creates a more realistic AI product workflow.

Pipeline flow:

Raw Data
 ↓
Data Cleaning
 ↓
Feature Engineering
 ↓
Model Training
 ↓
Prediction API
 ↓
Frontend Visualization

Frontend UI
 ↓
Flask API Backend
 ↓
Feature Engineering Pipeline
 ↓
Machine Learning Models
 ↓
Energy Consumption Forecast
 ↓
Prediction History & Export System

Light Mode

Dark Mode

Prediction Output

The project uses energy consumption telemetry data for forecasting future energy usage patterns.

• timestamp decomposition
• rolling averages
• temporal features
• environmental variables
• normalized energy metrics

• next-hour consumption prediction
• demand trend forecasting
• intelligent energy analytics
• operational energy planning

ENERGY-FORECASTING/
│── app/
│ └── energy_app.py
│
│── data/
│ ├── features/
│ │ ├── features_LSTM.csv
│ │ ├── features_MLP.csv
│ │ └── features_XGBOOST.csv
│ │
│ ├── processed/
│ │ ├── cleaned_energy_data.csv
│ │ └── feature_engineered_energy_data.csv
│ │
│ └── raw/
│ └── energy_data.csv
│
│── frontend/
│ ├── index.html
│ ├── style.css
│ └── control.js
│
│── models/
│ └── best_model.pkl
│
│── notebooks/
│ ├── 01. EDA.ipynb
│ ├── 02. Feature Engineering.ipynb
│ └── 03. Modelling.ipynb
│
│── screenshots/
│ ├── dark_mode.png
│ ├── light_mode.png
│ └── output.png
│
│── tests/
│ └── test_api.py
│
│── requirements.txt
└── README.md

The project includes automated API validation tests using PyTest.

• API endpoint validation
• prediction response testing
• input handling validation
• timestamp processing
• backend response integrity
• forecasting workflow verification

git clone https://github.com/girishshenoy16/AI-Energy-Forecasting-System.git
cd AI-Energy-Forecasting-System

python -m venv venv
venv\Scripts\activate

python3 -m venv venv
source venv/bin/activate

pip install --upgrade pip

pip install -r requirements.txt

Open and run the notebooks in order:

1. 01.EDA.ipynb
2. 02.Feature_Engineering.ipynb
3. 03. Modelling.ipynb

Execute notebooks and preprocessing workflows for:

• data cleaning
• feature engineering
• model training
• forecasting optimization

Then start the backend API.

python app/energy_app.py

Server starts at:

http://127.0.0.1:8000

Open:

frontend/index.html

The frontend automatically communicates with the Flask API backend.

{
 "current_energy": 320,
 "temperature": 28,
 "humidity": 60,
 "timestamp": "2025年11月19日 14:00"
}

{
 "prediction": 333.15,
 "timestamp_used": "2025年11月19日 14:00",
 "model": "xgboost"
}

This platform demonstrates how AI forecasting systems can help organizations:

• optimize energy consumption
• improve operational planning
• support sustainability initiatives
• forecast utility demand
• reduce energy inefficiencies
• improve infrastructure monitoring
• enable intelligent energy analytics

• deploy backend on cloud infrastructure
• real-time forecasting dashboards
• live chart visualizations
• compare multiple ML models dynamically
• user authentication system
• batch forecasting workflows
• smart grid integration
• energy anomaly detection

This project demonstrates practical experience in:

• full-stack AI engineering
• machine learning deployment
• Flask API development
• frontend integration
• cloud deployment workflows
• energy forecasting systems
• ML experimentation
• responsive UI engineering
• production-style AI architecture

Girish Shenoy

AI • Machine Learning • Full-Stack AI Systems • Forecasting Analytics • Intelligent Infrastructure

Please consider giving this repository a ⭐ on GitHub.