Energy consumption and carbon emission measurement online detection method based on hybrid particle swarm optimization algorithm

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.