The final, formatted version of the article will be published soon.
ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 |
doi: 10.3389/fenrg.2024.1505098
This article is part of the Research Topic Optimal Scheduling of Demand Response Resources In Energy Markets For High Trading Revenue and Low Carbon Emissions View all 29 articles
Augmented Pre-Training-based Carbon Emission Accounting Method Using Electricity Data under Small-Sample Condition
Provisionally accepted- 1 Grid Planning and Research Center, Guangdong Power Grid Corporation, CSG, Guangdong Guangzhou 510080, China, Guangzhou, China
- 2 Sichuan Energy Internet Research Institute Tsinghua University, Sichuan 610299, China, Chengdu, China
Accurate and rapid carbon accounting method for the power industry is crucial to support China's lowcarbon transformation. Currently, carbon emission accounting methods are based on slowly updated fuel statistics or expensive monitoring equipment, resulting in high costs and delays in carbon emission estimation. Power data offers high real-time availability, accuracy, and resolution, and exhibits a strong correlation with carbon emissions. These characteristics provide a pathway for achieving rapid and precise annual carbon emission accountings. However, carbon emission data inherently exhibits small sample characteristics, making these methods less effective in small sample conditions and leading to lower accounting accuracy. Therefore, this paper proposes an augmented pre-training-based "electricity-to-carbon" method under small sample conditions. This approach utilizes the correlation between electricity and carbon data as well as the autocorrelation characteristics of carbon emission data to construct a machine learning-based electricity-carbon fitting model for rapid and accurate carbon emission estimation. To address the challenges of small sample learning, this paper introduces an interpolation pre-training method to optimize the model's hyperparameters and conserve samples for model training, thereby improving the model's generalization and robustness. Case studies on a real dataset verifies the effectiveness of the proposed method.
Keywords: Carbon emission accounting, Small sample, machine learning, Data augmentation, Light gradient boosting machine
Received: 02 Oct 2024; Accepted: 25 Oct 2024.
Copyright: © 2024 Peng, Li, Yang, Feng and Gong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Bo Peng, Grid Planning and Research Center, Guangdong Power Grid Corporation, CSG, Guangdong Guangzhou 510080, China, Guangzhou, China
Yaodong Li, Grid Planning and Research Center, Guangdong Power Grid Corporation, CSG, Guangdong Guangzhou 510080, China, Guangzhou, China
Chen Yang, Sichuan Energy Internet Research Institute Tsinghua University, Sichuan 610299, China, Chengdu, China
Xianfu Gong, Grid Planning and Research Center, Guangdong Power Grid Corporation, CSG, Guangdong Guangzhou 510080, China, Guangzhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.