| 智能小区的商业智能 |
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| 引用本文： | 张素香. 智能小区的商业智能[J]. 北京邮电大学学报, 2012, 35(5): 94-97 |
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| 作者姓名： | 张素香 |
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| 作者单位： | 北京邮电大学网络技术研究院,北京100876;国网信息通信有限公司,北京100761 |
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| 摘 要： | 智能电网用电环节中智能小区的建设可以实现电网与用户之间的实时交互响应,提高用户需求侧响应水平,增强用户能效管理,实现电力负荷的削峰填谷.针对智能小区中的用户类型展开研究,基于支持向量机模型,提出了峰时耗电率、负荷率、用户配合度、谷电系数等特征,实验数据来自已建成的智能小区中的用户. 实验结果表明,基于支持向量机的电力用户类型判别方法是有效的.
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| 关 键 词： | 商业智能 支持向量机 特征选择 |
| 收稿时间： | 2011-12-23 |
Business Intelligence in the Smart Community |
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| ZHANG Su-xiang. Business Intelligence in the Smart Community[J]. Journal of Beijing University of Posts and Telecommunications, 2012, 35(5): 94-97 |
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| Authors: | ZHANG Su-xiang |
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| Affiliation: | 1. Institute of Network Technology, Beijing University of Posts and Telecommunications 2.State Grid Information and Telecommunication Company Limited |
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| Abstract: | Smart community construction in the smart power consumption link of the smart grid can bring real time interaction between the grid and end-users, improve demand response performance, enhance user energy efficiency management, and realize the load peak shaving. A new approach was proposed to recognize the resident user type in the smart community based on the support vector machine (SVM) classification model, some interesting features were discussed, which included the power consumption rate in the peak load period, load rate, user cooperation degree and so on. Experiment data were collected from the users of the smart community. Experimental results show that SVM is effective for the power resident user type. |
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| Keywords: | business intelligence support vector machine feature selection |
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