Equipment Recommendation System for Energy Efficiency Optimization
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Solution Overview
Problem
Current factory equipment control systems lack the ability to prioritize equipment use based on energy efficiency and idling states, leading to increased electricity consumption and costs.
Innovation Solution
An equipment recommendation method that generates feature variables from operation data, uses an idling state prediction model to determine idling data, and calculates energy efficiency indexes to suggest a use rank for each equipment, optimizing energy usage by considering both efficiency and idling data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If equipment is selected without considering energy efficiency and idling state, then equipment operation is simple, but electricity consumption increases and energy costs rise
Solution Approach 1:
The system performs preliminary analysis by generating feature variables from equipment operation information, predicting idling states using machine learning models, and calculating energy efficiency indexes before equipment selection. This advance preparation enables the equipment manager to make informed decisions about equipment prioritization, identifying which equipment should be operated first to minimize overall electricity consumption while avoiding the complexity of real-time optimization
Solution Approach 2:
The patent introduces an intermediary recommendation system that acts as a mediator between raw equipment data and management decisions. This system processes operation information, applies prediction models, and generates prioritization recommendations, thereby reducing the complexity burden on equipment managers while achieving energy-efficient equipment selection
2Loss of energy
If equipment prioritization is implemented without considering idling state, then energy efficiency improves, but energy waste from idling equipment increases
Solution Approach 1:
The system performs preliminary prediction of equipment idling states using trained machine learning models before final equipment selection. By analyzing feature variables and predicting whether equipment will remain idle, the system can prioritize equipment that is both energy-efficient and likely to be actively used, thereby reducing energy waste from idling while managing prediction model complexity through advance computation
Solution Approach 2:
The patent implements feedback mechanisms where prediction results and energy efficiency indexes are continuously updated based on actual equipment performance. This feedback loop allows the system to refine its prioritization strategy, improving energy waste reduction while adapting to changing equipment states and reducing the need for overly complex prediction models
Data Source
AI summary
An equipment recommendation method, an electronic device and a non-transitory computer readable recording medium are provided. A plurality of feature variables of each of equipment are obtained according to equipment operation information of the equipment. Idling data of each of the equipment is obtained according to an idling state prediction model and the feature variables of each of the equipment. A plurality of energy efficiency indexes of each of the equipment are calculated according to the equipment operation information of each of the equipment. A suggested used rank of each of the equipment is determined according to the plurality of energy efficiency indexes and the idling data of each of the equipment. Suggestion information related to the suggested used rank of each of the equipment is displayed via a display.