The Clapper

We built a smart HVAC system using the Gemini and weather APIs to optimize indoor comfort and cut energy waste by adapting to real-time user needs and changing weather conditions

Comment

Smart HVAC System: Gesture and AI Climate Control

Our smart HVAC system combines gesture control and AI interaction to deliver an intuitive, energy-efficient climate control experience.

Clap-Controlled Temperature Modes

We implemented a simple, hands-free input method using sound detection:

  • 1 Clap: Sets HVAC to Default Mode
  • 2 Claps: Switches to Heating Mode (Red LED indicator)
  • 3 Claps: Switches to Cooling Mode (Blue LED indicator)

This eliminates the need for remotes or touchscreens, making mode-switching seamless and accessible.

Gemini-Powered AI Interaction

We integrated the Gemini API to generate dynamic messages and responses:

  • AI responses are displayed on a connected OLED screen
  • Interaction adapts to user behavior and preferences

This layer of intelligence makes the system feel more personalized and responsive.

Inspiration

Our project was inspired by the growing need for energy-efficient climate control solutions that adapt to real-world conditions.

  • HVAC systems account for a significant portion of household energy usage
  • We wanted a smarter, greener alternative that adjusts automatically based on:
    • User preferences
    • Real-time weather data

What We Learned

Throughout this project, we deepened our understanding of:

  • Integrating external APIs (Gemini and Weather API) into hardware-based systems
  • Real-time data processing and decision-making logic
  • Efficient energy management strategies in smart homes
  • Collaborating effectively in a high-paced hackathon environment

How We Built It

Our system architecture included:

  • ESP32 microcontroller
  • Gemini API for natural language interaction and adaptive control logic
  • Weather API for real-time and forecasted climate data

Together, these enabled smart, adaptive HVAC operations tailored to both environment and user.

Challenges We Faced

  • Limited time for hardware testing and ensuring reliable automation
  • JSON parsing issues with Gemini API responses

Despite these hurdles, we built a fully functional prototype that demonstrates the power of AI-assisted energy optimization in HVAC systems.

Built With

Share this project:

Updates