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CasieNet: Category-aware and Side Information Enhanced Pre-training Network for Carbon Emission Prediction

Published: 26 July 2023 Publication History

Abstract

In the background of low carbon power, accurate prediction of carbon emission intensity and carbon emission source percentage of the power system can provide data support for carbon emission optimization strategy to achieve effective reduction of carbon emission of the power system. Most of the current methods adopt statistical methods and power flow analysis, etc. However, these approaches cannot make effective carbon emission prediction for specific generation patterns, and they are not suitable for use in real power systems that are affected by weather, wire state and energy storage element state, etc. under lossy conditions. For that reason, this paper proposes the CasieNet (Category-aware and side information enhanced) pre-training network for carbon emission prediction tasks. In order to fully learn the valid information in the raw power data, the model is pre-trained using a pre-training module, the local and global temporal feature information of the data is extracted using 1D CNN and Transformer, feature learning is performed by Local-Global window attention, and feature fusion is performed. Then, a Category-aware Attention module is proposed to assign weights to power data features in different generation modes and learn to contextual information, and then perform self-supervised learning. Finally, the prediction of carbon emission and carbon emission source percentage situation is carried out by combining the generation side information such as weather information, wire status information and energy storage element status information. The experiments show that the model can improve the accuracy of carbon emission prediction more effectively than the baseline model.

References

[1]
Wang Lingyun, Li Jiayong, Yang Bo.Low carbon economy operation of integrated energy system considering carbon emission of energy storage systems[J].Science Technology and Engineering, 2021, 21( 6) : 2334-2342.
[2]
Li Yansong, Liu Qizhi, Zhang zhenbo, Algorithm of carbon emission flow based on power distribution[J]. Power System Technology, 2017, 41( 3) : 840-844.
[3]
Wang Chaoqun, Chen Yi, Chi Changyun, Calculation Method of Power System Carbon Emission Flow Based on Power Flow Distribution Matrix[J]. Science Technology and Engineering, 2022, 22(12):4835-4842.
[4]
Chen Houhe, Mao Wenling, Zhang Rufeng, Low-carbon optimal scheduling of a power system source-load considering coordination based on carbon emission flow theory[J]. Power System Protection and Control, 2021, 49(10) : 1-11.
[5]
Zhou Tianrui, Kang Chongqing, Xu Qianyao, Analysis on distribution characteristics and mechanisms of carbon emission flow in electric power network[J]. Automation of Electric Power Systems, 2012, 36( 15) : 39-44.
[6]
GONG Yu, JIANG Chuanwen, LI Mingwei, Carbon emission calculation on power consumer side based on complex power flow tracing[J].Automation of Electric Power Systems, 2014, 38(17):113-117
[7]
Zhou Tianrui, Kang Chongqing, Xu Qianyao, Preliminary investigation on a method for carbon emission flow calculation of power system[J]. Automation of Electric Power Systems, 2012, 36( 11) : 44-49.
[8]
Feng Xin, Yang Jun. Improvement and enhancement of carbon emission flow theory considering power loss[J]. Electric Power Automation Equipment, 2016, 36( 5) : 81-86.
[9]
Kang C, Zhou T, Chen Q, Carbon emission flow from generation to demand: a network-based modeling[J]. IEEE Transactions on Smart Grid, 2015, 6( 5) : 2386-2394.
[10]
Tianrui Zhou, Chongqing Kang. Research on Low-carbon Oriented Optimal Operation of Distribution Networks Based on Carbon Emission Flow Theory[J]. Journal of Global Energy Interconnection,2019,2(03):241-247.
[11]
Baowei Li, Yonghua Song, Zechun Hu. Carbon Flow Tracing Method for Assessment of Demand Side Carbon Emissions Obligation[J]. IEEE Transactions on Sustainable Energy, 2013, 4(4): 1100-1107.
[12]
Baowei Li, Zechun Hu, Yonghua Song, Principle and Model for Assessment on Carbon Emission Intensity Caused by Electricity at Consumer Side[J]. Power System Technology, 2012, 36(8): 6-11.

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  1. CasieNet: Category-aware and Side Information Enhanced Pre-training Network for Carbon Emission Prediction

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      ICIAI '23: Proceedings of the 2023 7th International Conference on Innovation in Artificial Intelligence
      March 2023
      212 pages
      ISBN:9781450398398
      DOI:10.1145/3594409
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 26 July 2023

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