Open Access

ARTICLE

Research on Adaptive Dynamic Prediction Method for Carbon Emissions Based on Electricity Data Using GM(1,1)-BP Hybrid Modeling and FW-DDM Drift Correction

Rongfeng Deng¹, Xiaorui Hu^2,3, Yuliang Liu^2,3,*, Yuan Leng¹, Xi Liu¹, Shixu Zhang^2,3
1 Energy Development Research Institute, China Southern Power Grid Co., Ltd., Guangzhou, China
2 Department of Electrical Engineering, Tsinghua University, Beijing, China
3 Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu, China
* Corresponding Author: Yuliang Liu. Email:

Energy Engineering https://doi.org/10.32604/ee.2026.084302

Received 20 April 2026; Accepted 21 May 2026; Published online 07 July 2026

Abstract

Driven by the “Dual-Carbon” targets, precise, real-time, and cost-effective carbon emission monitoring has become imperative for formulating regional mitigation policies and evaluating compliance. However, conventional monitoring practices are often hindered by high costs, data latency, and the failure of static models to adapt to dynamic environmental shifts. To overcome these limitations, this study proposes an adaptive dynamic prediction framework grounded in an “electricity-to-carbon” mechanism. First, a hybrid Grey Model-Backpropagation (GM(1,1)-BP) neural network is developed to precisely model the non-linear coupling between power consumption and carbon emissions. Next, a Fuzzy-Weighted Drift Detection Method (FW-DDM) is integrated to monitor the prediction error stream in real time, triggering online model retraining immediately upon the detection of concept drift. Furthermore, Monte Carlo resampling is employed to quantify prediction uncertainty. Empirical results from Guangdong Province validate the feasibility of inferring carbon emissions from high-frequency electricity data, underscoring the proposed framework’s superior robustness in non-stationary environments. Finally, this paper discusses future research directions and the broader application prospects of “electricity-to-carbon” technologies at enhanced spatiotemporal resolutions.

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Keywords

Carbon emission prediction; electricity-to-carbon; adaptive correction; gray model; backpropagation neural network; drift detection method

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