HVAC Optimization Using Wi-Fi Presence Data and Predictive Analytics
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Solution Overview
Problem
Current HVAC systems in large institutions face inefficiencies due to the high cost, maintenance requirements, and lack of flexibility of CO2 sensors, which provide reactive and imprecise occupancy data, leading to energy waste and uncomfortable environments.
Innovation Solution
A system that uses existing Wi-Fi infrastructure to track device presence, integrating scheduling data and predictive analytics to provide real-time and proactive occupancy data, enabling dynamic HVAC control through machine learning and the BACnet protocol.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If CO2 sensors are deployed to detect occupancy, then occupancy detection capability is improved, but system cost and maintenance burden increase significantly
Solution Approach 1:
The patent uses Wi-Fi infrastructure to create a virtual copy of occupancy detection capability without deploying physical CO2 sensors. By analyzing Wi-Fi signal presence and characteristics, the system replicates occupancy detection functionality using existing network infrastructure, thereby avoiding the cost and maintenance burden of dedicated sensor deployments
Solution Approach 2:
The patent makes the Wi-Fi infrastructure perform multiple functions: it serves both its original purpose of network connectivity and the additional function of occupancy detection. By analyzing Wi-Fi association data, signal strength, and device presence, the system enables a single infrastructure to provide both networking and environmental monitoring capabilities
2Loss of information
If CO2 sensors are used for occupancy detection, then presence data is obtained, but the data is reactive and delayed
Solution Approach 1:
The patent performs preliminary occupancy detection by continuously monitoring Wi-Fi association data and device presence indicators before actual occupancy changes occur. The system proactively identifies when devices are approaching or leaving areas, enabling HVAC adjustments to be made in advance rather than reacting to CO2 level changes after they occur
Solution Approach 2:
The patent implements continuous feedback loops by constantly monitoring Wi-Fi network data, device associations, and signal characteristics. This real-time feedback enables the system to immediately detect occupancy changes and trigger appropriate HVAC responses without the delay inherent in CO2 sensor measurement and threshold-based triggering
3Area of stationary object
If CO2 sensors are deployed throughout the facility, then occupancy coverage is improved, but installation cost and wiring requirements increase
Solution Approach 1:
The patent leverages the Wi-Fi infrastructure's self-service capability to perform occupancy detection. The existing Wi-Fi access points and network infrastructure automatically collect and transmit device presence data, which the system then analyzes for occupancy information. This eliminates the need for separate sensor installation, wiring, and power requirements that would be necessary for CO2 sensor deployment
Solution Approach 2:
The patent merges the occupancy detection function with the existing Wi-Fi network infrastructure. By combining network connectivity and environmental monitoring into a single integrated system, the patent achieves facility-wide occupancy coverage without the additional installation costs and wiring requirements of separate sensor deployments
4Difficulty of detecting and measuring
If traditional occupancy sensors are used, then presence detection is achieved, but the system lacks flexibility for space reconfiguration
Solution Approach 1:
The patent implements dynamic occupancy detection by continuously tracking device locations through Wi-Fi signal analysis and association data. Unlike fixed sensors tied to specific locations, the Wi-Fi-based system dynamically follows devices as they move, automatically adapting to space reconfigurations, temporary event spaces, and changing occupancy patterns without requiring physical sensor relocation
Data Source
AI summary
Systems and methods for optimizing HVAC systems in large institutions are provided. Various embodiments of the technology provide systems and methods for determining and predicting building occupancy more accurately and in real-time to enhance energy usage and occupant comfort. Embodiments include a system and method that uses existing Wi-Fi infrastructure to collect wireless presence data, processes this data in a cloud-based environment, and employs machine learning algorithms to forecast occupancy. The system integrates scheduling data and can also incorporate CO2 sensor data to refine these predictions and dynamically adjust HVAC set points via the BACnet protocol.