Methods and systems for determining an optimal start and stop time of air handling units
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional HVAC systems lack the ability to optimize the start and stop times of Air Handling Units (AHUs) based on occupancy and temperature factors, leading to excessive energy use and occupant discomfort due to fixed scheduling without considering dynamic building conditions.
Innovation Solution
A method and system that utilizes a processor to determine optimal start and stop times for AHUs by integrating occupancy data, temperature data, and historical models, including linear, non-linear, and AI-based models, to calculate a weighted average for efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed schedule is used for AHU operation, then system simplicity is maintained, but energy consumption increases and occupant comfort deteriorates
Solution Approach 1:
The patent implements dynamic AHU scheduling by transitioning from fixed time-based operation to adaptive control that responds to real-time occupancy sensors, temperature sensors, and humidity sensors. The system dynamically adjusts AHU start/stop times and operational parameters based on actual building conditions, resolving the contradiction between system simplicity and energy efficiency.
Solution Approach 2:
The system incorporates multiple feedback loops using occupancy detectors, temperature sensors, and humidity sensors that continuously monitor building conditions and feed this information back to the control algorithm. This feedback mechanism enables the AHU operation to adapt to changing conditions, reducing energy consumption while maintaining comfort without requiring complex manual intervention.
2Ease of operation
If fixed schedule is used for AHU operation, then control simplicity is maintained, but occupant comfort deteriorates
Solution Approach 1:
The system employs an automated control algorithm that autonomously determines AHU scheduling decisions based on sensor inputs from occupancy detectors, temperature sensors, and humidity sensors. The algorithm self-adjusts operational parameters without requiring manual intervention, thereby maintaining ease of operation while significantly improving occupant comfort through responsive environmental control.
Solution Approach 2:
The control system transitions from static fixed scheduling to dynamic adaptive scheduling that automatically responds to real-time building conditions. The algorithm adjusts AHU operation based on actual occupancy patterns and environmental conditions, ensuring occupant comfort is maintained while keeping the system easy to operate through automated decision-making.
3Use of energy by moving object
If occupancy data and multiple models are integrated, then energy optimization improves, but system complexity increases
Solution Approach 1:
The patent segments the control system into distinct functional modules: occupancy detection subsystem, temperature sensing subsystem, humidity sensing subsystem, and a control algorithm that processes inputs from all sensors. This modular segmentation allows the system to integrate multiple data sources and models for energy optimization while managing complexity through organized, independent functional blocks that can be developed and maintained separately.
4Measurement precision
If real-time occupancy and temperature data are used, then AHU scheduling accuracy improves, but computational requirements increase
Solution Approach 1:
The control algorithm performs preliminary processing of occupancy and temperature data to identify patterns and predict future building conditions. By pre-processing sensor inputs and anticipating occupancy patterns, the system achieves high scheduling accuracy while reducing the computational burden during critical decision-making moments, as the algorithm has already prepared predictive information in advance.
Data Source
AI summary
A method and system for determining optimal start and stop time of Air Handling Units (AHU) are disclosed. The method comprises receiving, via at least one processor, a set of parameters associated with one or more zones in real time; determining an occupancy status of each of one or more zones based at least on received set of parameters; determining at least one start time and stop time for an Air Handling Unit (AHU) for each of one or more zones using historical data and each model of a plurality of models; determining a weighted average value for the determined at least one start time and stop time for the AHU based on a predefined weight allocated to each of at least one start time and stop time; and determining an optimal start and stop (OSS) time of AHU for each zone based at least on weighted average value.