| 基于电-碳转换因子的工业用户用电信息分级挖掘算法 |
投稿时间：2024-01-05 修订日期：2024-04-09  点此下载全文 |
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| 基金项目:国网宁夏电力有限公司科技项目资助 |
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| 中文摘要:以降低碳排放为目的，研究基于电-碳转换因子的工业用户用电信息分级挖掘算法，提升工业用户用电信息分级挖掘的全面性。利用潮流追踪法，结合工业用户用电信息，计算工业用户碳排放量；通过双向长短期循环记忆循环神经网络，结合工业用户用电信息，得到工业用户用电量；依据碳排放量与用电量，获取工业用户的电-碳转换因子，用于反映用电量与碳排放量之间的关联程度；通过模糊C均值聚类算法，分级挖掘电-碳转换因子，将具有相似电-碳转换因子的工业用户归为同一等级，完成工业用户用电信息分级挖掘。实验证明：该算法可有效计算工业用户碳排放量与用电量，得到电-碳转换因子；该算法可有效分级挖掘工业用户用电信息，确定工业用户的碳转换因子等级。 |
| 中文关键词:电-碳转换因子 工业用户 用电信息 分级挖掘 潮流追踪法 模糊C均值聚类 |
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| Hierarchical mining algorithm for industrial users' electricity consumption information based on electric-carbon conversion factor |
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| Abstract:In order to reduce carbon emission, a hierarchical mining algorithm for industrial users' electricity consumption information based on electric-carbon conversion factor is studied to improve the comprehensiveness of industrial users' electricity consumption information hierarchical mining. The power flow tracking method is used to calculate the carbon emission of industrial users by combining the electricity consumption information. The power consumption of industrial users is obtained by using the bidirectional long and short term cyclic memory cyclic neural network combined with the power consumption information of industrial users. According to carbon emission and electricity consumption, the electric-carbon conversion factor of industrial users is obtained to reflect the correlation degree between electricity consumption and carbon emission. By using the fuzzy C-means clustering algorithm, the industrial users with similar electric-carbon conversion factors are classified into the same level to complete the hierarchical mining of industrial users' electricity consumption information. Experimental results show that the algorithm can effectively calculate the carbon emission and electricity consumption of industrial users, and obtain the electric-carbon conversion factor. This algorithm can effectively mine the power consumption information of industrial users and determine the carbon conversion factor level of industrial users. |
| keywords:Electric-carbon conversion factor Industrial users Electricity consumption information Hierarchical mining Trend tracking method Fuzzy C-means clustering |
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