Title: Carbon emissions prediction method of industrial parks based on NSGA-II multi objective genetic algorithm

Authors: Peidong He; Xiaojun Li; Shuyu Deng; Yaxin Tu; Wenqi Shen

Addresses: State Grid Sichuan Electric Power Corporation Metering Center, 610045, Chengdu, Sichuan, China ' State Grid Sichuan Electric Power Corporation Metering Center, 610045, Chengdu, Sichuan, China ' State Grid Sichuan Electric Power Corporation Metering Center, 610045, Chengdu, Sichuan, China ' State Grid Sichuan Electric Power Corporation Metering Center, 610045, Chengdu, Sichuan, China ' State Grid Sichuan Electric Power Corporation Metering Center, 610045, Chengdu, Sichuan, China

Abstract: In order to address the significant discrepancies between the predicted results of existing industrial carbon emission forecasting methods and the actual results, this study investigates the prediction method of carbon emissions in industrial parks based on the NSGA-II multi-objective genetic algorithm. Firstly, the carbon emission prediction indicators are determined. Then, the normalisation method is applied to preprocess the indicator sample data and calculate the carbon emission prediction indicators for nine industrial parks. Lastly, based on the NSGA-II multi-objective genetic algorithm, non-dominated sorting and crowding distance are calculated to solve the objective function and achieve the prediction of carbon emissions in industrial parks. Through experimental verification, it has been demonstrated that the average absolute error of the prediction results in this study does not exceed 0.15, and the root mean square error remains below 0.10. This indicates that using the proposed method in this study can effectively reduce errors in carbon emission prediction for the industrial parks, resulting in good prediction performance.

Keywords: industrial park; prediction of carbon emissions; NSGA-II multi-objective genetic algorithm; fitness function.

DOI: 10.1504/IJETP.2024.141390

International Journal of Energy Technology and Policy, 2024 Vol.19 No.3/4, pp.286 - 301

Received: 09 Nov 2023
Accepted: 28 Feb 2024
Published online: 10 Sep 2024 *

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