TechGhar 🏠

Welcome to our project, an innovative, user-friendly web application designed to enhance energy efficiency within your home.

Problem Statement

Developing User Interface & Backend Services

Develop a user-friendly interface and robust backend services for seamless control and data analysis in the smart home system.

Machine Learning Optimization

Implement machine learning models to optimize device usage and energy consumption, enhancing system efficiency.

Real-Time Control and Monitoring

Ensure real-time control, automation, and performance monitoring features for a responsive and efficient smart home system.

Our Innovative Solutions

Unique Selling Point

The server is running on local network so there is no risk of breach of privacy. Moreover, our price is cheaper compared to our competitors.

Optimizing Energy Usage

We have trained our Machine Learning models so as to optimize the energy used by the appliances. This model also predicts faults and alerts the homeowner.

Community Driven

As this is open source it has a large and active community contributing to its development and thereby enhancing the product.

Size of Market

Global Smart Home Market Overview:

• 2020 Valuation: $78.3 billion
• 2025 Projection: $182.4 billion
• CAGR: 18.4%

DIY Smart Home Solutions:

• Market Share: 15-20% of the total smart home market
• Example: Home Assistant

Open-Source Platforms:

• Represent a smaller subset of the DIY market.

Key Competitive Advantages

Expert Team

Our skilled team brings specialized knowledge and experience to provide top-notch solutions tailored to our clients' needs.

Customer Focus

We prioritize building strong relationships and understanding our clients' needs, leading to long-term partnerships based on trust and satisfaction.

Cutting-Edge Technology

We use the latest tools and technology to stay ahead, ensuring efficient and effective services.

Proven Success

With a track record of successful projects, we've earned a reputation for reliability and excellence, setting us apart from the competition.

ML Model Explained

1. Data cleaning
2. Data transformation
3. Data visualization
4. Time series analysis
   1. Auto correlation
   2. Seasonality
   3. Stationarity
5. Anomaly detection
6. Prediction using ARIMA model

Our project:

Team members:

• Rishi Joshi
• Mohit Hingorani
• Samaksh Gupta
• Riya Agrawal

PPT Drive Link

https://www.canva.com/design/DAGLe7xTObU/w3BaPuT9bGzg6i5jb917NA/view?utm_content=DAGLe7xTObU&utm_campaign=designshare&utm_medium=link&utm_source=editor