Research on Carbon Emission Calculation of Cement Industry Based on Electric Power Flow
张卫玲① ZHANG Wei-ling；张佳艺① ZHANG Jia-yi；方兵① FANG Bing；
王善立① WANG Shan-li；谭杰仁②TAN Jie-ren；张婉莹②ZHANG Wan-ying
（①海南省电网公司，海口 570100；②中国能源建设集团广东省电力设计研究院有限公司，广州 510663）
（①Hainan Power Grid Company，Haikou 570100，China；②China Energy Engineering Group Guangdong Electric Power Design and Research Institute Co.，Ltd.，Guangzhou 510663，China）
摘要：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。
Abstract: 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.
关键词：电力大数据；水泥行业；碳排放测算
Key words: power big data；cement industry；carbon emission calculation
中图分类号：X322;F426 文献标识码：A 文章编号：1006-4311（2024）27-081-04 doi:10.3969/j.issn.1006-4311.2024.27.024
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