Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM

Lihua Zhong, Feng Pan (), Yuyao Yang, Lei Feng, Haiming Shao and Jiafu Wang
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Lihua Zhong: Metrology Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, China
Feng Pan: Metrology Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, China
Yuyao Yang: Metrology Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, China
Lei Feng: Metrology Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, China
Haiming Shao: National Institute of Metrology of China, Beijing 100029, China
Jiafu Wang: National Institute of Metrology of China, Beijing 100029, China

Energies, 2025, vol. 18, issue 13, 1-28

Abstract: Carbon emission estimation for power systems is essential for identifying emission responsibilities and formulating effective mitigation measures. Current carbon emission prediction methods for power systems exhibit limited computational efficiency and inadequate noise immunity under complex operating conditions. In this study, we address these limitations by improving population initialization, search mechanisms, and iteration strategies and developing a hybrid strategy Modified Dung Beetle Optimization (MDBO) algorithm. This led to the development of an MDBO-enhanced Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) network hybrid prediction model for carbon emission prediction. Firstly, the theoretical calculation mechanism of carbon emission flow in power systems is analyzed. Subsequently, an MDBO-CNN-LSTM deep network architecture is constructed, with detailed explanations of its fundamental structure and operational principles. Then, the proposed MDBO-CNN-LSTM model is utilized to predict the nodal carbon emission factor of power systems with the integration of renewable energy sources. Comparative experiments with conventional CNN-LSTM models are conducted on modified IEEE 30-, 118-, and 300-bus test systems. The results show that the maximum mean squared error of the proposed method does not exceed 0.5734% in the strong-noise scenario for the 300-bus system, which is reduced by half compared with the traditional method. The proposed method exhibits enhanced robustness under strong noise interference, providing a novel technical approach for precise carbon accounting in power systems.

Keywords: power system; carbon emission factor prediction; modified dung beetle optimization; convolutional neural network; long short-term memory network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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