Fan Control Model for Building Ventilation Energy Optimization
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Solution Overview
Problem
Existing building management systems struggle to efficiently control room air parameters while minimizing electrical energy consumption for fan operation.
Innovation Solution
A method and control system that utilize a machine learning-based model creation device to determine a control model, correlating room air parameters, environmental air parameters, and fan operating parameters, to optimize fan control and reduce energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional control methods are used to maintain room air parameters, then the room air parameters can be maintained within ranges, but electrical energy consumption for fan operation is high
Solution Approach 1:
The system performs preliminary actions by using sensors to detect current room air parameters and environmental conditions, then uses a machine learning model to predict future parameter trends and determine optimal fan operating parameters in advance, allowing the fan to be controlled at energy-minimizing settings while still maintaining air parameters within acceptable ranges
Solution Approach 2:
The system changes the operational parameters of the fan dynamically based on predicted room air parameter trends. The machine learning model determines optimal fan operating parameters (such as speed or on/off timing) that minimize electrical energy consumption while ensuring room air parameters remain within acceptable ranges, adapting to changing environmental conditions
2Use of energy by moving object
If fan operation is minimized to reduce energy consumption, then electrical energy usage decreases, but room air parameters may deviate from predefined ranges
Solution Approach 1:
The system implements feedback control by continuously monitoring current room air parameters with sensors, comparing them against acceptable ranges, and using the machine learning model to adjust fan operating parameters accordingly. The model learns from historical data how fan operations affect room air parameters, enabling it to predict the outcome of different control actions and select those that maintain temperature within ranges while minimizing energy consumption
Solution Approach 2:
The machine learning model performs preliminary analysis of environmental conditions and current room air parameters to predict future temperature trends, allowing the system to proactively adjust fan operation before temperature deviations occur, thus maintaining comfort while reducing energy usage
Data Source
AI summary
The present disclosure refers to a method for operating a flow producing unit as well as a control system comprising at least one flow producing unit. The flow producing unit is particularly a rigidly installed system in a building. The flow producing unit has a fan control, which controls a fan for producing an airflow. A control model is provided to fan control, which defines a correlation between a room air parameter, an environmental air parameter, as well as a fan operating parameter for fan. By means of measurement of a room air measurement value for the room air parameter and a measurement of an environmental air measurement value for the environmental air parameter, a suitable fan operating parameter for control of fan can be selected and adjusted based on control model. Thereby the fan operating parameter is selected so that the required electrical energy for operating the fan is minimum.