Intelligent management system for efficiency improvement and carbon emission reduction of electric appliance

Through hybrid modeling and machine learning technology, combined with multi-source sensor networks and intelligent control strategies, the accuracy and real-time problems in the energy efficiency and carbon emission management of electrical equipment have been solved, and high-precision energy efficiency evaluation and accurate realization of carbon emission reduction targets have been achieved.

CN120671978APending Publication Date: 2025-09-19JIANGSU LINGLANXING CARBON NEUTRAL TECH CO LTD
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Patent Information

Application Number
CN202510760190.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-09
Publication Date
2025-09-19

AI Technical Summary

Technical Problem

Existing technologies have problems of low accuracy and poor real-time performance in the energy efficiency and carbon emission management of electrical equipment. Traditional modeling methods are difficult to adapt to fluctuations in the carbon intensity of the power grid and changes in process flow, resulting in large errors in energy efficiency diagnosis and inaccurate carbon emission predictions, and are unable to meet the real-time optimization needs of the high-frequency trading market.

Method used

Hybrid modeling technology is used, combining Monte Carlo simulation and BP neural network to generate high-fidelity physical constraints. Data is collected in real time through a multi-source sensor network, and machine learning algorithms are used for data analysis and evaluation to generate intelligent control strategies to achieve coordinated optimization of energy efficiency and carbon emissions.

Benefits of technology

It improves the prediction accuracy of carbon emission factors, reduces energy efficiency assessment errors, achieves precise optimization of equipment operation and precise realization of carbon emission reduction targets, and improves energy efficiency improvement rate and return on emission reduction investment.

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Abstract

The invention relates to the technical field of energy conservation, in particular to an intelligent management system for electric appliance efficiency improvement and carbon emission reduction, which comprises a data acquisition and monitoring module, a data analysis and evaluation module, an intelligent optimization control module and a user interaction and management module, compared with the problems of low precision and poor real-time performance caused by adoption of a static carbon emission factor method in the prior art, the technical breakthrough is realized through hybrid modeling: Monte Carlo simulation generates million-level working condition samples based on Latin hypercube sampling, and high-fidelity physical constraints are constructed in combination with an energy flow balance equation; the defect of insufficient sample coverage of a traditional method is overcome; the BP neural network captures a process-power grid-environment nonlinear coupling relation through thousand groups of sample training, a dynamic weight adjustment mechanism responds to power grid carbon intensity fluctuation in real time, and the limitation that a static model cannot adapt to real-time working conditions is broken through; the confidence interval output mechanism quantifies the prediction uncertainty, and compared with traditional point estimation, the decision reliability is improved.
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