Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM
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 operati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/13/3491 |
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| _version_ | 1850118308682006528 |
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| author | Lihua Zhong Feng Pan Yuyao Yang Lei Feng Haiming Shao Jiafu Wang |
| author_facet | Lihua Zhong Feng Pan Yuyao Yang Lei Feng Haiming Shao Jiafu Wang |
| author_sort | Lihua Zhong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3661936d310d4952a7e01ad7f671f3b5 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-3661936d310d4952a7e01ad7f671f3b52025-08-20T02:35:54ZengMDPI AGEnergies1996-10732025-07-011813349110.3390/en18133491Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTMLihua Zhong0Feng Pan1Yuyao Yang2Lei Feng3Haiming Shao4Jiafu Wang5Metrology Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, ChinaMetrology Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, ChinaMetrology Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, ChinaMetrology Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, ChinaNational Institute of Metrology of China, Beijing 100029, ChinaNational Institute of Metrology of China, Beijing 100029, ChinaCarbon 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.https://www.mdpi.com/1996-1073/18/13/3491power systemcarbon emission factor predictionmodified dung beetle optimizationconvolutional neural networklong short-term memory network |
| spellingShingle | Lihua Zhong Feng Pan Yuyao Yang Lei Feng Haiming Shao Jiafu Wang Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM Energies power system carbon emission factor prediction modified dung beetle optimization convolutional neural network long short-term memory network |
| title | Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM |
| title_full | Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM |
| title_fullStr | Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM |
| title_full_unstemmed | Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM |
| title_short | Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM |
| title_sort | nodal carbon emission factor prediction for power systems based on mdbo cnn lstm |
| topic | power system carbon emission factor prediction modified dung beetle optimization convolutional neural network long short-term memory network |
| url | https://www.mdpi.com/1996-1073/18/13/3491 |
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