Virtual Energy Measurement for Manufacturing Equipment Coverage
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Solution Overview
Problem
Current methods for measuring energy consumption in manufacturing environments are inefficient, particularly for individual units, leading to increased operational costs and challenges in assessing equipment efficiency due to aging, and lack accuracy in energy consumption reporting.
Innovation Solution
A method and system utilizing a unit energy consumption prediction model to estimate energy consumption without installing devices on every piece of equipment, combined with a recommending method to strategically install measuring devices where prediction errors exceed a threshold, reducing costs and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If energy consumption measuring device is installed on each piece of manufacturing equipment, then measurement precision is improved, but device complexity and operational cost increase
Solution Approach 1:
The patent segments the measurement approach by dividing manufacturing equipment into different types and groups, applying measurement devices selectively to representative equipment of each group rather than every single piece of equipment. This segmentation allows the system to achieve comprehensive energy consumption data while reducing the total number of devices needed.
Solution Approach 2:
The patent uses copying by creating virtual models or proxy representations of energy consumption data from measured equipment and applying them to similar unmeasured equipment. By copying measurement results from representative samples to the broader equipment population, the system achieves comprehensive coverage without installing devices on every piece of equipment.
2Measurement precision
If energy consumption measuring device is installed on each piece of manufacturing equipment, then measurement precision is improved, but operational cost increases
Solution Approach 1:
The patent segments the equipment population into measurable representative samples and unmeasured groups, reducing the operational cost by only installing measurement devices on the segmented representative samples while deriving data for the entire equipment fleet through analysis and modeling.
Solution Approach 2:
The patent applies partial action by installing measurement devices on only a portion of equipment (the representative samples) rather than all equipment, which is sufficient to achieve the measurement precision goal while significantly reducing operational costs.
3Device complexity
If virtual measurement approach is used to reduce device installation, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by continuously comparing virtual measurement results with actual measurements from representative equipment, using the discrepancies to refine and update the virtual models. This feedback loop ensures that measurement precision is maintained or improved over time while keeping device complexity low.
Solution Approach 2:
The patent applies preliminary action by pre-establishing virtual measurement models and relationships between equipment types before actual measurement needs arise. These pre-built models enable accurate predictions without requiring extensive device installation, thus maintaining precision while reducing complexity.
4Loss of energy
If selective device installation is implemented, then operational cost is reduced, but measurement coverage decreases
Solution Approach 1:
The patent applies universality by creating a measurement system that, through representative sampling and virtual modeling, provides energy consumption data for all equipment types using a limited set of physical devices. The system achieves universal coverage across diverse equipment while maintaining cost efficiency.
Solution Approach 2:
The patent transitions from physical measurement coverage to virtual/digital coverage by creating computational models that extend measurement capability beyond the physical locations of installed devices. This dimensional shift from physical to virtual space allows comprehensive coverage without proportional increases in device quantity.
Data Source
AI summary
A method of predicting energy consumption, performed by a computing device, includes: obtaining historical manufacturing data, wherein each piece of historical manufacturing data includes total energy consumption and historical performance values corresponding to manufacturing conditions, respectively, each of manufacturing conditions includes at least one of an equipment type and a product type, and each of historical performance value indicates at least one of an equipment operation duration and a product quantity; training and generating unit energy consumption prediction model using the historical manufacturing data; obtaining at least one piece of unit energy consumption of at least one manufacturing conditions using default performance values corresponding to manufacturing conditions and unit energy consumption prediction model; and outputting at least one piece of unit energy consumption. The present disclosure further provides a system of predicting energy consumption and recommending method of installing energy consumption measuring device.