Equipment Replacement Recommendation From Abnormal Power Consumption
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Solution Overview
Problem
Existing equipment with low energy efficiency often goes undetected, leading to increased electricity consumption and costs due to wear and tear, making timely replacement difficult.
Innovation Solution
An electricity consumption prediction model is used to estimate reference consumption based on historical operation data, identify abnormal consumption intervals, and provide a recommended equipment list for replacement when certain conditions are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If equipment is operated continuously without monitoring, then productivity is maintained, but energy efficiency deteriorates due to wear and tear
Solution Approach 1:
The system continuously monitors equipment operation information and electricity consumption, comparing actual values against predicted values to detect deviations indicating wear and tear. This feedback loop enables timely identification of energy efficiency degradation while maintaining continuous operation.
Solution Approach 2:
The electricity consumption prediction model estimates future energy consumption patterns based on historical data and operation conditions. By predicting abnormal consumption before it significantly impacts energy efficiency, the system enables proactive replacement scheduling that minimizes productivity disruption.
2Productivity
If equipment replacement is delayed, then productivity is maintained, but electricity consumption increases due to low energy efficiency
Solution Approach 1:
The system provides continuous feedback on equipment energy performance by comparing actual electricity consumption against predicted consumption for equivalent production output. This enables real-time identification of equipment that is consuming excessive energy, allowing scheduled replacement that balances productivity with energy cost management.
Solution Approach 2:
The equipment effectively monitors its own energy consumption patterns and operational status, automatically generating replacement recommendations when energy efficiency thresholds are breached. This self-monitoring capability enables autonomous energy management without requiring external intervention for each assessment.
3Loss of energy
If equipment monitoring is implemented, then energy efficiency is improved through timely detection, but device complexity increases
Solution Approach 1:
The patent replaces complex manual monitoring and analysis systems with an automated computational model that processes equipment operation information and electricity consumption data. The prediction model and abnormality detection algorithms substitute for manual energy audits and equipment inspections, reducing operational complexity while improving detection accuracy.
Solution Approach 2:
The system creates a virtual model of equipment energy consumption patterns through the prediction model, which replicates expected behavior under various operating conditions. This digital twin approach allows virtual testing and comparison without requiring physical modifications to the equipment, simplifying the monitoring infrastructure.
4Device complexity
If manual equipment replacement decision-making is used, then device complexity is kept low, but loss of time occurs due to delayed detection of inefficient equipment
Solution Approach 1:
The system automatically generates replacement recommendations by comparing actual equipment performance against predicted benchmarks, eliminating the need for manual energy audits and equipment assessments. This automated decision-support mechanism reduces the time required to identify inefficient equipment while keeping the overall system relatively simple through rule-based algorithms.
Solution Approach 2:
The system provides continuous feedback on equipment energy performance, automatically alerting users when replacement should be considered. This eliminates the time lag associated with manual discovery of energy efficiency issues, enabling timely replacement decisions without requiring complex continuous monitoring infrastructure.
Data Source
AI summary
The disclosure provides a recommended method for replacing equipment and an electronic device. A plurality of reference electricity consumptions of an equipment are estimated by using an electricity consumption prediction model based on equipment operation information of the equipment in a plurality of unit periods. A plurality of actual electricity consumptions of the equipment in the multiple unit periods are obtained. An abnormal electricity consumption time interval is determined by comparing the actual electricity consumptions with the reference electricity consumptions. A replacement index and an equipment energy efficiency of the equipment in the abnormal electricity consumption time interval are determined according to the equipment operation information of the equipment in the abnormal electricity consumption time interval. In response to determining that the replacement index and the equipment energy efficiency meet a replacement condition, a recommended equipment list including at least one recommended equipment is provided.