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5 July 2024 A self extraction method for carbon emission spatial pattern differences based on incremental learning and electricity big data
Hongwei Han, Dongge Zhu, Wang Su, Wenni Kang, Jiangbo Sha
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Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318444 (2024) https://doi.org/10.1117/12.3033184
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia
Abstract
This article proposes a self extraction method for carbon emission spatial pattern differences based on incremental learning and electricity big data. This article first preprocesses power big data, including data cleaning, format conversion, and other operations to eliminate noise and outliers in the data. Then, incremental learning algorithms are used to learn from the preprocessed data and extract the spatial pattern differences of carbon emissions. Incremental learning algorithms can update model parameters in real-time, adapt to changes in data, and improve the accuracy and real-time performance of feature extraction. By analyzing the time series and spatial distribution characteristics of carbon emission data, it is possible to quickly and accurately identify abnormal values and trend changes in carbon emissions, providing scientific basis for carbon emission control and environmental protection. The experimental results show that this method can effectively extract the spatial pattern differences of carbon emissions and improve the accuracy of carbon emission monitoring. The highest MAE value is only 0.03, the highest Recall value is 99.58%, and the success rate is over 98%, which can better meet the actual needs of carbon emission monitoring.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Hongwei Han, Dongge Zhu, Wang Su, Wenni Kang, and Jiangbo Sha "A self extraction method for carbon emission spatial pattern differences based on incremental learning and electricity big data", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318444 (5 July 2024); https://doi.org/10.1117/12.3033184
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KEYWORDS
Carbon
Machine learning
Feature extraction
Education and training
Data modeling
Support vector machines
Pollution control
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