HVAC Control Using Depth-Sensor Occupancy Detection for Energy Savings
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Solution Overview
Problem
HVAC systems lack efficient methods to dynamically adjust heating, ventilation, and air conditioning based on the number of occupants in a space, leading to inefficient energy use and comfort control.
Innovation Solution
Implementing a system that uses depth sensors and motion/visual sensors to count and track occupants, learning their activity patterns to adjust HVAC settings before and after occupancy, using depth segmentation and head detection methods to provide real-time and predictive control of environmental comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If HVAC systems continuously operate to maintain environmental comfort, then comfort control is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary actions by predicting future occupancy and activity levels using statistical models and machine learning algorithms. HVAC settings are adjusted in advance based on predicted occupancy patterns, allowing the system to prepare for upcoming changes rather than reacting to current conditions alone. This enables energy-saving pre-conditioning of spaces before occupants arrive while maintaining comfort standards.
Solution Approach 2:
The system enables self-service through automated occupancy detection using depth sensors and motion sensors, coupled with statistical modeling that autonomously predicts occupancy patterns. The HVAC control adjusts settings based on this self-generated occupancy information without requiring manual input or continuous human monitoring, allowing the system to serve itself in optimizing energy consumption while maintaining comfort.
2Loss of energy
If HVAC systems adjust settings based on real-time occupancy detection, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces statistical models and machine learning algorithms as intermediaries between simple sensor inputs and HVAC control decisions. These intermediary computational layers process raw occupancy data from depth and motion sensors, transforming it into predictive occupancy patterns that drive HVAC adjustments. This intermediary processing layer simplifies the overall control logic while enabling sophisticated energy-saving strategies.
Solution Approach 2:
The system replaces complex mechanical occupancy detection methods with optical depth sensors and motion sensors combined with statistical computing. Instead of using multiple sensors or complex mechanical detection systems, the patent uses lightweight computational models that process data from standard sensors to achieve accurate occupancy prediction and HVAC control optimization.
3Speed
If HVAC systems use predictive modeling based on historical data, then responsiveness to occupancy changes is improved, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary computational work by building statistical models from historical occupancy data in advance. These pre-trained models can quickly predict occupancy changes without requiring real-time high-precision measurements for each decision. The system uses accumulated historical patterns to make rapid predictions about future occupancy, enabling fast responsiveness while reducing the precision burden on individual sensor measurements.
Solution Approach 2:
The system changes parameters by transitioning from requiring high-precision real-time occupancy measurements to using statistical probability distributions based on historical data. Instead of demanding exact occupancy counts at every moment, the system works with aggregated statistical patterns and probability models, allowing faster response times with relaxed measurement precision requirements while maintaining effective HVAC control.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise control of HVAC systems based on actual occupancy, improving comfort and reducing energy consumption by anticipating and responding to changes in occupancy and activity levels.
Implementation Method 1
The depth sensor may include an infrared laser projector coupled to a monochrome complementary metal-oxide-semiconductor (CMOS) sensor configured to capture three-dimensional video data under varying ambient light conditions.
Data Source
AI summary
Embodiments of methods and apparatus disclosed herein may employ depth, visual, or motions sensors to enable three-dimensional people counting and data mining to enable an energy saving heating, ventilation, and air conditioning (HVAC) control system. Head detection methods based on depth information may assist people counting in order to enable an accurate determination of room occupancy. A pattern of activities of room occupancy may be learned to predict the activity level of a building or its rooms, reducing energy usage and thereby providing a cost savings.