Article: Prediction of carbon emissions throughout the lifecycle of zero carbon substations based on Lasso-GRNN neural network model Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.8 pp.1 - 19 Abstract: In order to solve the problems of low recall rate, low prediction accuracy, and long prediction completion time of carbon emission prediction factors in traditional methods, a prediction method of carbon emissions throughout the lifecycle of zero carbon substations based on Lasso-GRNN neural network model is proposed. Lasso-GRNN neural network model is constructed by using the key indicators for predicting the carbon emissions of a zero carbon substation throughout its entire lifecycle as input variables and the carbon emission values as output variables. The model uses Lasso to screen key indicators and inputs them into the GRNN neural network to obtain accurate prediction results. Experimental results show that the proposed method has a maximum recall rate of 98.12% for the influencing factors of carbon emissions throughout the entire life cycle of zero carbon substations, a maximum prediction accuracy of 98.51%, and a minimum prediction completion time of 0.68s. Inderscience Publishers - linking academia, business and industry through research

Title: Prediction of carbon emissions throughout the lifecycle of zero carbon substations based on Lasso-GRNN neural network model

Authors: Ting Zeng; Yueqing Chen; Liuhuo Wang; Mingpeng Yuan; Zhangqi Lv; Dianbin Wang

Addresses: Shantou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Shantou, 515041, Guangdong, China ' Shantou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Shantou, 515041, Guangdong, China ' Guangdong Power Grid Co., Ltd., Guangzhou, 510699, Guangdong, China ' Shantou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Shantou, 515041, Guangdong, China ' Shantou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Shantou, 515041, Guangdong, China ' Northeast Electric Power University, Jilin, 132012, Jilin, China

Abstract: In order to solve the problems of low recall rate, low prediction accuracy, and long prediction completion time of carbon emission prediction factors in traditional methods, a prediction method of carbon emissions throughout the lifecycle of zero carbon substations based on Lasso-GRNN neural network model is proposed. Lasso-GRNN neural network model is constructed by using the key indicators for predicting the carbon emissions of a zero carbon substation throughout its entire lifecycle as input variables and the carbon emission values as output variables. The model uses Lasso to screen key indicators and inputs them into the GRNN neural network to obtain accurate prediction results. Experimental results show that the proposed method has a maximum recall rate of 98.12% for the influencing factors of carbon emissions throughout the entire life cycle of zero carbon substations, a maximum prediction accuracy of 98.51%, and a minimum prediction completion time of 0.68s.

Keywords: Lasso-GRNN neural network model; zero carbon substation; full lifecycle; carbon emissions forecast; indicators.

DOI: 10.1504/IJBIDM.2026.153567

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.8, pp.1 - 19

Received: 25 Apr 2025
Accepted: 30 Sep 2025
Published online: 14 May 2026 *

Title: Prediction of carbon emissions throughout the lifecycle of zero carbon substations based on Lasso-GRNN neural network model

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