Machine-learning method for conditioning individual or shared areas
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Solution Overview
Problem
Current HVAC systems for maintaining thermal comfort in indoor locations are inefficient and costly, relying on manual controls and basic temperature sensors, which fail to accurately adjust to the comfort needs of occupants, leading to high energy consumption.
Innovation Solution
A computer-implemented method and system that retrieves environmental and outdoor conditions, calculates thermal comfort using user-specific data and feedback, and adjusts HVAC operations based on predicted mean vote and user satisfaction, incorporating machine-learning techniques and thermal profiling to optimize energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HVAC systems are manually controlled or controlled by basic temperature sensors, then the system operation is simple, but the thermal comfort accuracy and energy efficiency deteriorate
Solution Approach 1:
The patent implements feedback mechanisms by collecting user comfort feedback and environmental sensor data, then using this information to continuously adjust HVAC operations. The system monitors thermal comfort conditions and uses this feedback to optimize heating and cooling operations, improving accuracy while managing complexity through automated control loops.
Solution Approach 2:
The patent replaces manual mechanical control systems with automated computer-implemented control systems that use algorithms to calculate thermal comfort and control HVAC operations. This substitution of mechanical/manual systems with automated computational systems improves measurement precision and control accuracy.
2Reliability
If HVAC systems operate continuously to maintain thermal comfort, then user comfort is improved, but energy consumption increases
Solution Approach 1:
The patent applies partial action by adjusting HVAC operations based on actual thermal comfort needs rather than continuous full-capacity operation. The system calculates the precise heating or cooling required to achieve thermal comfort, avoiding excessive energy consumption while maintaining reliability through on-demand adjustments.
Solution Approach 2:
The patent changes operational parameters dynamically by adjusting temperature setpoints, airflow rates, and equipment operation schedules based on calculated thermal comfort conditions. This allows the system to maintain thermal comfort reliability while optimizing energy consumption through parameter optimization rather than continuous full-operation.
3Adaptability or versatility
If basic temperature sensing is used for HVAC control, then the system complexity is low, but the adaptability to individual user comfort needs deteriorates
Solution Approach 1:
The patent segments the control system into multiple independent components: environmental sensing, user feedback collection, thermal comfort calculation, and HVAC control. This segmentation allows the system to achieve high adaptability to individual user needs while managing complexity through modular, independent functional blocks that can operate and be adjusted separately.
Data Source
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AI summary
A method and system for conditioning an interior area is disclosed. A method includes retrieving environmental conditions regarding the interior area, wherein the environmental conditions include temperature, humidity, and air speed; retrieving outdoor environmental conditions; generating a field of environmental conditions at a plurality of points within the interior area; estimating clothing insulation of a user based on the outdoor environmental conditions; calculating a thermal comfort of the user to determine a predicted mean vote; and operating a heating, ventilation, and air conditioning system based on the calculated thermal comfort.