HVAC system having learning and prediction modeling
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Solution Overview
Problem
Current HVAC systems in multi-unit dwellings operate in an open-loop configuration, leading to energy inefficiency, unstable temperatures, excessive drafts, and reduced comfort due to uncoordinated components.
Innovation Solution
A networked HVAC system with localized controllers in each unit, connected via cloud networks and local interconnections, monitors temperature, air flow, humidity, and occupancy to optimize energy usage and temperature control across multiple zones and common areas, using learning and predictive modeling to adapt to real-time and anticipated conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If HVAC components are operated independently in open-loop configuration, then each component can function autonomously, but energy efficiency deteriorates and temperature stability worsens
Solution Approach 1:
The patent implements a closed-loop control system where the central controller receives real-time data from sensors monitoring temperature, humidity, air quality, and occupancy across all zones. This feedback mechanism enables the controller to dynamically adjust HVAC component operations to optimize energy efficiency while maintaining comfort, directly resolving the contradiction between autonomous operation and energy efficiency.
Solution Approach 2:
The central controller serves multiple functions simultaneously: it coordinates HVAC components, monitors environmental conditions, processes occupancy data, predicts future conditions using machine learning, and adjusts operations across all zones. This multi-functionality allows the system to maintain autonomous component operation while achieving system-wide energy optimization.
2Ease of operation
If HVAC components are operated independently in open-loop configuration, then each component can function autonomously, but temperature stability deteriorates
Solution Approach 1:
The closed-loop control system continuously monitors temperature in each zone and provides feedback to the central controller, which adjusts HVAC operations to maintain stable temperatures. This real-time feedback and adjustment mechanism resolves the contradiction by enabling autonomous operation while ensuring temperature stability through coordinated control.
Solution Approach 2:
The machine learning models predict future environmental conditions and occupancy patterns, allowing the system to take preliminary actions to maintain temperature stability before conditions change. This predictive capability enables the system to prepare in advance, maintaining stability while components operate autonomously based on predicted rather than reactive control.
3Device complexity
If HVAC components are operated independently in open-loop configuration, then system complexity is reduced, but comfort level deteriorates due to excessive drafts and temperature variations
Solution Approach 1:
The system uses sensors to continuously monitor temperature, humidity, and air flow in each zone, providing feedback to the central controller. This enables real-time detection and correction of harmful conditions like drafts and temperature variations, resolving the contradiction by implementing coordinated control that maintains comfort without excessive complexity.
Solution Approach 2:
The system implements zone-specific control where each zone's HVAC operations are independently optimized based on local conditions such as occupancy, temperature, and air quality. This local quality approach allows the system to address comfort issues in each zone individually, reducing harmful effects like drafts and temperature variations while maintaining manageable system complexity through modular zone control.
4Device complexity
If HVAC components are operated independently in open-loop configuration, then component control is simplified, but energy usage increases due to uncoordinated operation
Solution Approach 1:
The central controller performs multiple functions including coordinating HVAC components, monitoring environmental conditions, processing occupancy data, and implementing energy optimization strategies. This multi-functionality enables the system to achieve coordinated energy-efficient operation without proportionally increasing control complexity, as the same controller manages multiple aspects of system operation.
Solution Approach 2:
The machine learning models automatically learn from historical data and make autonomous decisions about optimal HVAC operations, reducing the need for complex manual control coordination. The system serves itself by automatically optimizing energy usage based on learned patterns, resolving the contradiction between control simplicity and energy efficiency.
Data Source
AI summary
A networked HVAC system is implemented as part of a multi-unit dwelling. At least one HVAC unit is installed within each unit of the multi-unit dwelling. Each individual HVAC unit includes a localized HVAC unit controller, which is connected to an external network. The HVAC unit controller also can be connected to any other controllable HVAC devices in the unit, such as an exhaust fan. The HVAC system addresses the open-loop issue by monitoring temperature, air flow, humidity, air pressure, occupancy, window open/close state, and HVAC units of all units in a multi-unit dwelling, as well as common areas, and optimizing operating parameters to minimize wide swings in operational states and managing the overall system of multiple units/common areas so that energy usage and temperature/ventilation control is optimized. The HVAC system also enables learning and predictive modeling for adapting to real-time and anticipated condition requirements within each zone.