Adaptive comfort control system
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current HVAC systems inefficiently manage comfort and energy usage due to reliance on single temperature controls, failing to account for various environmental and occupancy factors, leading to inconsistent comfort and high energy consumption.
Innovation Solution
A comfort management system with a processor-controlled device that monitors multiple environmental parameters and adjusts HVAC operations to maintain a defined comfort zone range, considering factors like temperature, humidity, occupancy, and activity levels, using sensors and communication with a network to optimize comfort and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single temperature set point is used to control HVAC systems, then the control system is simple and easy to operate, but comfort consistency across different conditions and locations deteriorates
Solution Approach 1:
The patent divides the building into multiple zones with individual temperature set points and control capabilities. Each zone can be independently controlled based on its specific conditions, occupancy patterns, and environmental factors, allowing comfort consistency across different locations while maintaining manageable complexity through modular control.
Solution Approach 2:
The system dynamically adjusts temperature set points based on real-time conditions including occupancy detection, outdoor temperature, humidity levels, and predictive algorithms. This dynamic adaptation allows the system to maintain comfort consistency across varying conditions without requiring manual reconfiguration, balancing simplicity with responsiveness.
2Adaptability or versatility
If multiple environmental parameters are monitored and adjusted, then comfort adaptability improves, but device complexity increases
Solution Approach 1:
The HVAC control system integrates multiple functions including temperature control, humidity management, occupancy detection, predictive algorithms, and energy optimization into a single unified platform. This multi-functionality allows the system to adapt to various environmental parameters and occupancy conditions while presenting a simplified user interface, effectively managing complexity through integration rather than proliferation of separate systems.
Solution Approach 2:
The system employs predictive algorithms and machine learning capabilities to automatically adjust environmental parameters based on learned occupancy patterns, weather forecasts, and historical data. This self-service approach allows the system to adapt to multiple conditions autonomously without requiring complex manual configuration or constant user intervention, maintaining high adaptability while keeping operational complexity low.
3Reliability
If HVAC systems operate to maintain strict temperature set points, then occupant comfort is maximized, but energy consumption increases
Solution Approach 1:
The system uses occupancy detection to apply HVAC control only in zones and time periods when occupants are present, rather than maintaining strict temperature set points throughout the entire building continuously. This partial action approach maintains comfort where needed while significantly reducing energy consumption in unoccupied areas, balancing comfort provision with energy efficiency.
Solution Approach 2:
The system dynamically adjusts temperature set points based on predictive algorithms that consider weather forecasts, occupancy patterns, and thermal mass of building materials. By anticipating temperature changes and pre-heating or pre-cooling spaces before occupancy, the system maintains comfort while reducing the need for high-energy operation during peak demand periods, effectively lowering overall energy consumption.
4Use of energy by moving object
If predictive algorithms are implemented to forecast environmental changes, then energy optimization improves, but measurement and detection difficulty increases
Solution Approach 1:
The system uses readily available external data sources such as weather service APIs, building management system integrations, and occupancy sensors as intermediaries to provide input data for predictive algorithms. These intermediaries translate complex environmental measurements into standardized formats that the prediction system can process, reducing the difficulty of direct environmental parameter sensing while enabling sophisticated energy optimization through predictive control.
Data Source
AI summary
There is provided a comfort management system including a networked comfort management control device. The comfort management control device operates an HVAC interface to maintain an environment utilizing a determined comfort zone range for one or more occupants of an area treated by the HVAC system, and utilizes controlled deviations from an initial set point to maintain comfort while maximizing energy efficiency of the HVAC system.