Reinforcement Learning-Based Smart Home Energy Management System

2 min read

Summary

This project involves developing an AI-powered energy management system for smart homes. The system uses a Raspberry Pi Zero with various sensors to optimize HVAC and lighting control in real-time, reducing energy consumption while maintaining user comfort. A prototype is being built with custom hardware including environmental sensors, relays, and an mmWave human detection sensor.

Technologies Used

• Raspberry Pi Zero (runs the AI model)
• WiFi-enabled microcontroller (controls relay inputs)
• Reinforcement learning model (for optimization)
• BME 280 temperature sensor
• C1001 mmWave human detection sensor/PIR sensor
• ACS712 current sensors (8 channels)
• 8-channel relay module
• Voltage sensors
• CAD design for enclosure
• Custom firmware

Challenges

• Integrating multiple sensor inputs (temperature, motion, current)
• Developing accurate reinforcement learning model with limited training data
• Power management for all components
• Real-time processing on Raspberry Pi Zero hardware constraints
• Sensor fusion from different data sources
• Physical assembly and enclosure design
• Ensuring data privacy in home environment

Results

• Completed hardware purchases and CAD designs
• Partially assembled prototype with working sensor inputs
• Developed initial firmware and AI model architecture
• Demonstrated 30% energy savings in simulation
• Created functional circuit diagrams and system architecture

Lessons Learned

• Component costs can escalate quickly in hardware projects
• Sensor calibration requires more time than anticipated
• Raspberry Pi Zero has limitations for real-time AI applications
• Proper enclosure design is crucial for sensor accuracy
• Voltage/current monitoring needs careful circuit design
• Project management becomes complex with hardware/software integration

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