Judgment Model of Pollution Source Excessive Emission Based on LightGBM

Wenhao Ou (), Xintong Zhou (), Zhenduo Qiao (), Liang Shan (), Zhenyu Wang () and Jiayi Chen ()
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Wenhao Ou: State Grid Commercial Big Data Co., Ltd
Xintong Zhou: State Grid Commercial Big Data Co., Ltd
Zhenduo Qiao: State Grid Commercial Big Data Co., Ltd
Liang Shan: State Grid Commercial Big Data Co., Ltd
Zhenyu Wang: State Grid Commercial Big Data Co., Ltd
Jiayi Chen: State Grid Commercial Big Data Co., Ltd

A chapter in LISS 2022, 2023, pp 325-335 from Springer

Abstract: Abstract Big data has become a social consensus to improve the modernization level of national governance, support the innovation of government management and social governance modes. This paper aims to strengthen the application of big data in the field of ecological environment. It monitors and analyzes abnormal production behaviors based on energy consumption data. Thus, it assists regulatory authorities to improve supervision efficiency and further protects the legitimate rights of legal enterprises, maintains market fairness, and help the country win the defense of blue sky. By participating in the 5th Digital China Innovation Contest(DCIC), whose topic is ‘Pollution Source Excessive Emission Research and Judgment’, we proposed a multi-feature model for pollution source excessive emission using LightGBM. The final F1 score of model is 0.61203524.

Keywords: Big data in electric power industry; Pollution prevention; Excessive emission; LightGBM; DCIC 2022 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-99-2625-1_25

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