A novel carbon emission estimation method based on electricity‑carbon nexus and non-intrusive load monitoring

Xia, Yingqi; Sun, Gengchen; Wang, Yanfeng; Yang, Qing; Wang, Qingrui; Ba, Shusong

doi:10.1016/j.apenergy.2024.122773

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…By processing and analyzing the electricity data of China's flat glass industry, the study built an electricity-carbon model using support vector regression (SVR), and experimentally verified the validity and accuracy of the model, which proved that it is effective to use electricity data for carbon emission modeling. Xia et al [23] proposed an innovative carbon emission estimation method based on the correlation between electricity and carbon emissions and non-intrusive load monitoring (NILM), which is dedicated to improving the accuracy and interpretability of carbon emission estimation in the field of electricity production. The core of the methodology is to decompose the total power consumption of the enterprise, specify the power consumption of each piece of key equipment, and calculate the carbon dioxide emissions accordingly.…”

Section: Related Workmentioning

confidence: 99%

The Optuna–LightGBM–XGBoost Model: A Novel Approach for Estimating Carbon Emissions Based on the Electricity–Carbon Nexus

Cai,

Feng,

Wang

et al. 2024

Applied Sciences

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With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for “measuring carbon through electricity”, in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals.

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Section: Related Workmentioning

confidence: 99%

The Optuna–LightGBM–XGBoost Model: A Novel Approach for Estimating Carbon Emissions Based on the Electricity–Carbon Nexus

Cai,

Feng,

Wang

et al. 2024

Applied Sciences

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show abstract

“…Extracting important non-linear features from long-term time series data is often necessary. Traditional machine learning algorithms struggle to learn the complex non-linear relationships between electricity consumption and carbon emissions, making precise carbon monitoring difficult [19]. Therefore, it is crucial to fully mine and extract feature information from power data and enhance learning algorithm robustness.…”

Section: Introductionmentioning

confidence: 99%

High-Frequency Carbon Emission Monitoring for Power Generation Enterprises Based on ICEEMDAN- Inception-Transformer

Jiang,

Mao

2024

Preprint

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Accurate carbon accounting is essential for power generation enterprises to participate in carbon markets and implement carbon reduction strategies. However, due to excessive reliance on detailed energy data and manual calculations, carbon emission accounting in power generation enterprises suffers from low frequency, significant lag, and poor reliability. Some evidences suggest a strong correlation between internal carbon emissions and electricity consumption in power generation enterprises. Inspired by them, this paper proposes a novel model, named ICEEMDAN-Inception-Transformer, to thoroughly explore the relationship between power data and carbon emissions, providing precise hourly carbon emission acquisition for power enterprises. This model first utilizes ICEEMDAN to extract the significant characteristics of power data, then employs advanced Inception and Transformer structures to capture the complex high-dimensional features of the "electricity-carbon" correlation, thereby realizing enterprise carbon emissions monitoring. The model was extensively validated on three datasets from three different types of power enterprises. The average performance on indicators of RMSE, MAE, MAPE, and R2 of the model on the three datasets reached 11.69 tCO2, 9.58 tCO2, 2.44%, and 96.42%, respectively. The results demonstrate that the proposed monitoring model possesses certain advantages in terms of the accuracy and robustness of acquiring enterprise carbon emissions, providing valuable insights for high-frequency accurate carbon monitoring in power generation enterprises.

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Future energy insights: Time-series and deep learning models for city load forecasting

Maleki,

Lundström,

Musaddiq

et al. 2024

Applied Energy

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A novel carbon emission estimation method based on electricity‑carbon nexus and non-intrusive load monitoring

Cited by 6 publications

References 17 publications

The Optuna–LightGBM–XGBoost Model: A Novel Approach for Estimating Carbon Emissions Based on the Electricity–Carbon Nexus

The Optuna–LightGBM–XGBoost Model: A Novel Approach for Estimating Carbon Emissions Based on the Electricity–Carbon Nexus

High-Frequency Carbon Emission Monitoring for Power Generation Enterprises Based on ICEEMDAN- Inception-Transformer

Future energy insights: Time-series and deep learning models for city load forecasting

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