Machine Learning Control for Industrial Facility Energy Settings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industrial facilities face challenges in optimizing energy efficiency due to complex interdependencies among equipment and environmental factors, making it difficult to determine optimal settings for resources like power and cooling, which traditional engineering formulas often fail to capture effectively.
Innovation Solution
A machine learning system integrated with a control system that receives state data from industrial facilities, predicts optimal settings for resource efficiency, and adjusts settings automatically to improve efficiency without requiring extensive user input or testing, using neural networks trained through reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional engineering formulas are used to optimize industrial facility settings, then the system is simple and easy to understand, but it fails to capture complex interdependencies among equipment and environmental factors
Solution Approach 1:
The patent replaces traditional engineering formulas (mechanical/mathematical models) with a machine learning model that learns optimal settings from historical data. The ML model captures complex non-linear relationships and interdependencies among equipment and environmental factors without requiring explicit mathematical formulations, thereby improving optimization accuracy while maintaining reasonable system complexity.
2Measurement precision
If machine learning models are used to predict optimal settings, then efficiency optimization accuracy improves, but the system complexity and difficulty of implementation increase
Solution Approach 1:
The machine learning model is trained offline using historical facility data to automatically learn optimal settings patterns. During operation, the trained model autonomously predicts optimal settings without requiring continuous human intervention or complex real-time computations, enabling the system to maintain high optimization accuracy while keeping operational complexity manageable.
Solution Approach 2:
The machine learning model is trained in advance using historical data from the industrial facility before deployment. This preliminary training phase allows the model to learn complex relationships and patterns offline, so that during actual operation, it can quickly provide accurate predictions without requiring complex real-time processing or extensive user input.
3Measurement precision
If extensive user input and testing are required to optimize settings, then control accuracy can be improved, but time consumption and operational burden increase
Solution Approach 1:
The machine learning model autonomously predicts optimal facility settings based on current conditions without requiring extensive user input or manual testing. The system automatically processes historical data and environmental inputs to generate recommended settings, significantly reducing the time and effort required compared to traditional manual optimization approaches.
Data Source
AI summary
Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.