Estimation of Carbon Emissions from Green Grid Infrastructure Projects Based on PSO-LM-BP

Due to the lack of comprehensive understanding of the key characteristics of the “photovoltaic storage direct flexible” system in power grid infrastructure projects, it is difficult to reduce the losses generated during power transmission, resulting in significant difficulty in estimating carbon emissions. Therefore, a green power grid infrastructure project carbon emission estimation based on PSO-LM-BP is proposed. The carbon emission distribution calculation based on PQ decomposition method is completed through the trend distribution algorithm, and a BP neural network is introduced to construct a carbon emission estimation model. The LM algorithm is used to perform secondary optimization on the ellipsoid parameters, and the PSO algorithm is applied to obtain the global optimal particle for carbon emission estimation, optimizing the emission estimation model. The experimental results show that the carbon emission estimation error of the research method is always below <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 2\ \text{t CO 2}$</tex>, and the correlation coefficient of the estimation results remains above 8.5, indicating a high overall estimation accuracy.