Belnadino/SmartHome-AI-IoT-Regressor
SmartHome-AI-IoT Regressor Model
Overview
The SmartHome-AI-IoT Regressor Model predicts the desired air-conditioner temperature setting based on smart-home contextual information.
The model enables adaptive HVAC control by considering environmental and user-related conditions.
Authors
Belnadino Mgimba Department of Computer Science, Durham University, Durham, United Kingdom
Anish Jindal Department of Computer Science, Durham University, Durham, United Kingdom
Project
This model is part of the:
SmartHome-AI-IoT Framework "A Natural Language Understanding, Low-Latency, Context-Aware, Multi-Device Control and Self-Learning SmartHome IoT Framework"
Developed for research on:
- Natural language understanding for IoT
- Context-aware smart-home automation
- Edge AI deployment
- Human-in-the-loop adaptive systems
Intended Use
Applications include:
- Smart HVAC automation
- Context-aware temperature control
- Energy-efficient smart homes
- Edge IoT systems
Model Architecture
Model: XGBoost Regression
Input features:
- Room location
- Time period
- Ambient temperature
- Occupancy
Output: Predicted AC temperature
Example:
Input: Room: Bedroom Time: Night Ambient temperature: 22°C Occupancy: 1
Output: AC temperature: 23°C
Dataset
Training data generated from:
SmartHomeIoTNLU Dataset
Dataset characteristics:
- 200,320 smart-home events
- Environmental context
- Device states
- AC temperature parameters
Training Configuration
XGBoost parameters:
Performance
Deployment
The model can be deployed in:
- Raspberry Pi edge gateway
- Smart-home controllers
- IoT automation frameworks
Limitations
- Temperature preferences are generated from synthetic rules.
- Real occupants may have different comfort preferences.
- Multi-user preference conflicts are not considered.
Citation
SmartHome-AI-IoT Framework
License
apache-2.0