Machine learning based control systems for heating ventilation and cooling systems
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Solution Overview
Problem
Existing HVAC systems lack efficient control mechanisms to dynamically adjust to changing temperature, humidity, and flow rate conditions, leading to suboptimal performance and energy inefficiency.
Innovation Solution
A machine learning-based control system that utilizes sensors to collect data on temperature, humidity, and flow rates, generates features from this data, and trains a machine learning process to predict future operational values, allowing for proactive adjustments to control settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional HVAC control systems are used, then the system structure is simple and easy to operate, but the system cannot dynamically adjust to changing conditions leading to suboptimal performance and energy inefficiency
Solution Approach 1:
The patent replaces traditional mechanical control systems with a machine learning-based control system that uses sensors, processors, and algorithms to dynamically optimize HVAC operation. The machine learning model processes sensor data (temperature, humidity, flow rates) and generates optimized control signals, substituting simple mechanical thermostats with an intelligent digital control system that adapts to changing conditions.
Solution Approach 2:
The machine learning control system enables the HVAC system to self-optimize by continuously learning from sensor data and automatically adjusting operational parameters. The system trains machine learning models using historical sensor data and autonomously generates control decisions without requiring manual intervention or complex user programming, allowing the system to improve its performance over time through self-learning.
2Loss of energy
If traditional HVAC control systems are used, then the control mechanism is simple, but energy efficiency is poor due to inability to predict and adapt to future conditions
Solution Approach 1:
The machine learning control system performs preliminary actions by predicting future environmental conditions and HVAC performance metrics before they actually occur. The system uses trained machine learning models to forecast temperature, humidity, and energy consumption patterns, allowing the HVAC system to proactively adjust operational parameters in advance to optimize energy efficiency and maintain comfort conditions.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor actual environmental conditions and system performance, this data is fed back to the machine learning model which then adjusts control decisions. The feedback mechanism compares predicted versus actual outcomes and uses this information to refine future predictions and control actions, creating a closed-loop system that continuously improves energy efficiency.
3Use of energy by stationary object
If machine learning-based control is implemented, then energy efficiency and performance are optimized, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the control system into distinct functional modules: sensor data acquisition, feature extraction, machine learning model training, prediction generation, and control signal output. This modular segmentation allows each component to be optimized independently and facilitates easier implementation and maintenance of the complex machine learning-based control system while achieving superior energy efficiency.
Data Source
AI summary
The disclosure relates to machine learning and artificial intelligence based control systems for heating ventilation and cooling systems. In some examples, a computing device receives flow data characterizing flow rates of a first fluid over a time range. The computing device also receives humidity data characterizing humidity levels over the time range. Further, the computing device receives temperature data characterizing temperatures over the time range. The computing device also generates a training set of features based on the flow data, the humidity data, and the temperature data. The computing device further trains a machine learning process based on the training set of features. The computing device stores machine learning model data characterizing the trained machine learning process in a data repository.