Occupancy-Based HVAC and Lighting Control With Self-Learning Setup
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Solution Overview
Problem
Current building control systems for lighting, shades, and HVAC are costly due to labor-intensive installation and maintenance, with complexity adding significantly to overall costs, and existing solutions do not effectively reduce these expenses while providing intelligent control.
Innovation Solution
A system that uses adaptive algorithms to self-learn human behaviors from various sensors, allowing for non-labor-intensive installation and maintenance by identifying occupancy patterns, generating behavioral profiles, and calculating spatial models to anticipate and actuate system outputs based on occupant behavior, reducing the need for professional installation and configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If professional installation and configuration systems are used, then system control intelligence is improved, but installation and maintenance costs increase significantly
Solution Approach 1:
The system performs self-configuration and self-learning automatically without requiring professional installers. Occupancy sensors and behavioral algorithms enable the system to adapt to building layouts and user patterns autonomously, eliminating the need for costly professional installation and ongoing maintenance configuration services.
Solution Approach 2:
The patent replaces manual configuration processes with automated software-based learning algorithms. Instead of requiring technicians to physically configure each sensor and control point, the system uses computational algorithms to automatically map building spaces and learn occupancy behaviors, substituting mechanical installation labor with intelligent software processing.
2Adaptability or versatility
If complex control systems are deployed, then system functionality is improved, but installation time and labor requirements increase
Solution Approach 1:
The system performs preliminary learning and configuration automatically during initial operation without requiring time-consuming professional installation. The self-learning algorithms begin capturing occupancy patterns and building spatial relationships immediately upon deployment, eliminating the need for separate configuration phases and reducing overall installation time.
Solution Approach 2:
Complex manual configuration tasks are replaced with automated computational processes. The system uses software-based spatial mapping and behavioral learning algorithms to rapidly establish control parameters, substituting time-intensive manual setup with efficient automated processing that scales without proportionally increasing installation time.
3Loss of energy
If traditional occupancy control systems are installed, then energy savings are achieved, but the cost of installation exceeds the energy savings realized
Solution Approach 1:
The system eliminates the need for expensive professional installation by performing self-configuration automatically. This dramatically reduces the upfront installation cost barrier, making the system economically viable where traditional systems would cost more to install than the energy savings they would generate over their operational lifetime.
Solution Approach 2:
The patent employs cost-effective wireless occupancy sensors and simplified control architecture that reduce hardware and installation expenses. By using affordable sensor technology and eliminating complex wiring requirements, the system achieves energy savings that substantially exceed the reduced installation costs, creating a favorable economic return.
Data Source
AI summary
A system for automatic setup and control of Lighting, Heating Ventilation and Air Conditioning systems comprising a set of occupancy detectors and at least one controller where the controller determines various aspects of the building control based on the input of the occupancy detectors.