Controlling device and method for HVAC system
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Solution Overview
Problem
Traditional HVAC systems lack adaptability to local conditions, leading to reduced thermal comfort and increased power consumption due to fixed setting values, which are not tailored to environmental factors like temperature and humidity.
Innovation Solution
A controlling device and method for HVAC systems that establish a building heat load model using envelope data, integrate field and setup data through an optimization module, and adjust the HVAC system to optimize indoor environment settings, utilizing algorithms like LQR or LQG to minimize energy consumption while maintaining thermal comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predetermined fixed values are used for HVAC control, then the system operation is simple, but thermal comfort is reduced and power consumption is increased
Solution Approach 1:
The patent implements dynamic control by continuously adjusting HVAC system parameters based on real-time environmental data (temperature, humidity, solar radiation, wind speed) and building heat load characteristics. The controller dynamically optimizes supply air temperature, airflow rate, and system operation modes, transforming the static fixed-value control into an adaptive dynamic system that responds to changing conditions, thereby reducing energy consumption while maintaining thermal comfort.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring environmental parameters (indoor temperature, humidity, outdoor temperature, solar radiation, wind speed) and using this information to adjust HVAC operations. The controller receives real-time data from sensors and modifies control parameters accordingly, creating a closed-loop control system that optimizes energy efficiency based on actual environmental conditions and building heat load requirements.
2Device complexity
If predetermined fixed values are used for HVAC control, then the control system is simple, but adaptability to local conditions is reduced
Solution Approach 1:
The patent changes control parameters dynamically based on environmental conditions and building characteristics. The system adjusts supply air temperature, airflow rate, and operational modes according to real-time measurements of temperature, humidity, solar radiation, and wind speed. This parameter adaptation allows the HVAC system to respond to local conditions without requiring complex structural modifications to the control architecture.
Solution Approach 2:
The system performs preliminary calculations of building heat load using envelope data and environmental parameters before executing control actions. By pre-computing heat load requirements based on building characteristics and forecasted environmental conditions, the system can optimize HVAC operations in advance, improving adaptability while managing control complexity through structured computational approaches.
3Use of energy by stationary object
If energy conservation measures are implemented, then power consumption is reduced, but thermal comfort may be compromised
Solution Approach 1:
The system dynamically adjusts control parameters (supply air temperature, airflow rate, system modes) based on real-time environmental conditions and calculated building heat load. By optimizing these parameters according to actual conditions rather than using fixed conservative settings, the system achieves energy conservation while maintaining thermal comfort through precise, condition-based control adjustments.
Solution Approach 2:
The patent implements dynamic optimization that continuously adapts HVAC operations to current environmental conditions and building heat load requirements. This dynamic approach allows the system to reduce energy consumption during favorable conditions (e.g., mild outdoor temperatures, low solar radiation) while maintaining comfort standards, and to adjust appropriately when conditions change, ensuring reliability of thermal comfort throughout varying operational scenarios.
Data Source
AI summary
The present disclosure discloses a controlling device and method for a Heating Ventilation and Air Conditioning system. The controlling device includes a building heat load module, a data collecting module, an optimization module, and a controller. A building heat load model is established according to building envelope data. Field information data and setup data are collected. The building heat load model, the field information data, and the setup data are integrated to produce optimal setup data by optimal computation. According to the optimal setup data, the HVAC system proceeds to modulate room temperature, room humidity, or gas concentration to achieve functions of maintaining indoor thermal comfort and energy conservation.