工业用户连续参与需求响应的用户基线负荷精准计算方法OA北大核心CSTPCD
Accurate Estimation Method of Customer Baseline Load for Continuous Participation of Industrial Users in Demand Response
提出了一种将K-means聚类分析与长短期记忆神经网络算法结合,通过工业同源组信息进行迁移学习优化的计算方法.该方法实现了对长时间连续参与需求响应的工业用户基线负荷的精准计算,提高了工业用户需求响应效果评价的准确性.通过城市级虚拟电厂平台采集的参与需求响应实践的工业用户电力负荷数据,验证了该方法的有效性.
A computational method combining K-means cluster analysis with long-and short-term memory neural network algorithm is proposed,and transfer learning is carried out by industrial homogeneous group information to further optimize the estimation effect.Accurate estimation of industrial customer power baseline load under long-term continuous response is realized,and the accuracy of the demand response effect evaluation of industrial customers is improved.The effectiveness of the method is verified by the load data of industrial customers participating in demand response practice collected by the city-level virtual power plant platform.
梁珩;黄耕;侯宾;杨玺;罗小虎;张达
清华大学能源环境经济研究所,北京 100084北京邮电大学电子工程学院,北京 100876清华四川能源互联网研究院,四川成都 610000
需求响应用户基线负荷计算聚类分析
demand responsecustomer baseline load estimationcluster analysis
《中国电力》 2024 (003)
34-42 / 9
国家自然科学基金资助项目(71974109,72140005). This work is supported by National Natural Science Foundation of China(No.71974109,No.72140005).
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