基于智能算法的煤改电用户负荷识别

建设泛在电力物联网使得为用户提供更加多样化和个性化的服务成为可能,近两年来煤改电工程发展迅速,如何通过用户负荷曲线对煤改电用户进行识别成为一个研究热点。论文首先深入剖析了现阶段煤改电工程取得的成绩以及存在的问题,运用大数据技术与泛在电力物联网技术可以很好地解决煤改电进程中存在的矛盾与问题。以某地区煤改电用户负荷特性为例描述了在采用蓄热式电锅炉取暖后的用电负荷特性的变化,通过构建粒子群优化后的支持向量机模型,对某地区电网冬季典型日用电负荷数据进行识别与分类,通过测试集的验证,论文建立的模型具有较高的识别精度,平均准确率达到98%,具有一定的实际价值。

Load identification of coal to electricity users based on intelligent algorithm

The construction of ubiquitous power Internet of things makes it possible to provide users with more diversified and personalized service. In recent years, with the rapid development of the coal to electricity project, how to identify the coal to electricity users through the user load curve has be-come a research hotspot. First of all, the paper deeply analyzes the achievements and problems of the coal to electricity project at this stage. Using big data technology and ubiquitous power Internet of things technology can solve the contradictions and problems in the process of coal to electricity. Taking the load characteristics of coal to electricity users in a certain area as an example, the change of load characteristics after heating with regenerative electric boiler is described. By constructing the support vector machine model after particle swarm optimization, the typical daily power load data of a certain area in winter are identified and classified, Through the verification of the test set, the model established in this paper has a high recognition accuracy, with an average accuracy of 98%, which has a certain practical value.