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9 January 2024 Energy consumption and carbon emission measurement online detection method based on hybrid particle swarm optimization algorithm
Tianyi Zhang, Huaiying Shang, Angang Zheng
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Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129690N (2024) https://doi.org/10.1117/12.3014377
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China
Abstract
The detection data of energy consumption and carbon emissions may be affected by equipment failure, sensor error, incomplete data collection and other factors, resulting in low detection accuracy. Based on this, an online detection method of energy consumption and carbon emissions measurement based on hybrid particle swarm optimization algorithm is proposed. Analyze the measurement principles of energy consumption and carbon emissions. On this basis, collect the measurement data of energy consumption and carbon emissions in real time, use semi-supervised learning to extract the measurement operation data, calculate the Angle between the newly generated optimization solution and the reference direction vector, and use it as the attribute space of particle update, and assign all optimization target values, and use the hybrid particle swarm optimization algorithm. The on-line measurement process based on hybrid particle swarm optimization algorithm is completed. The experimental results show that the proposed method has advantages in all aspects of performance index, AUC value is higher than 0.9, detection time is lower than 8s.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Tianyi Zhang, Huaiying Shang, and Angang Zheng "Energy consumption and carbon emission measurement online detection method based on hybrid particle swarm optimization algorithm", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129690N (9 January 2024); https://doi.org/10.1117/12.3014377
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KEYWORDS
Carbon
Particle swarm optimization
Detection and tracking algorithms
Data modeling
Carbon dioxide
Energy efficiency
Machine learning
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