一种基于深度学习的双维绿电识别与动态碳核算方法
By employing a deep learning-based two-dimensional green electricity identification method, which combines multi-dimensional fusion identification of protocols and physical features with dynamic emission factor calculation, the problems of insufficient reliability in green electricity identification and poor timeliness in carbon emission accounting are solved, thereby improving the accuracy of green electricity identification and enabling real-time dynamic updates of carbon accounting.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NINGXIA LGG INSTR CO LTD
- Filing Date
- 2026-05-09
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies suffer from problems such as insufficient reliability due to reliance on single-dimensional information for green electricity identification, coarse processing of electrical quantity characteristics, and poor granularity and timeliness due to static lag in carbon emission accounting.
A deep learning-based two-dimensional green electricity identification method is adopted. The green electricity identification signal is received by power line carrier and triple-verified. The core protocol feature parameters are extracted and normalized by combining multi-dimensional physical features. The green electricity purity index is determined by using a physical feature grouping interactive convolution model and dynamic correction factor. The dynamic emission factor is calculated by combining a carbon accounting model.
It significantly improves the accuracy and anti-interference ability of green electricity identification, enhances the differentiation accuracy of different green electricity types, and realizes real-time dynamic updates of carbon emission accounting and closed-loop calculation of carbon quotas at the terminal.
Smart Images
Figure CN122153493B_ABST