基于电力流的水泥行业碳排放测算研究

Research on Carbon Emission Calculation of Cement Industry Based on Electric Power Flow

高耗能行业及重点用能企业的低碳转型对于双碳目标能否顺利实现起到了关键作用,本研究基于某县水泥行业构建了能耗预测和碳排放测算模型,并对比了传统预测算法和智能预测算法,发现智能预测算法的预测效果要优于传统预测算法,智能算法中PB神经网络模型的预测效果最佳,训练集和测试集的拟合优度高于0.98,模型构建良好,体现了良好的预测能力,也证实了利用电力大数据配合其他指标具备开展水泥行业碳排放预测工作的可行性。

The low-carbon transformation of high energy consuming industries and key energy consuming enterprises plays a key role in the smooth achievement of the dual carbon goals.This study constructs energy consumption prediction and carbon emission measurement models based on the cement industry in a certain county,and compares traditional prediction algorithms and intelligent prediction algorithms.It is found that the prediction effect of intelligent prediction algorithms is better than that of traditional prediction algorithms.The PB neural network model in intelligent algorithms has the best prediction effect,and the fitting goodness of the training and testing sets is higher than 0.98.The model is well constructed,reflecting good prediction ability,and also confirming the feasibility of using electricity big data with other indicators to carry out carbon emission prediction work in the cement industry.