| 基于边缘计算的智能电能表能耗与寿命优化方法 |
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| 引用本文： | 刘林青,马红明,李鹏,段子荷,李梦宇,邓芳明.基于边缘计算的智能电能表能耗与寿命优化方法[J].电测与仪表,2023,60(5):173-179. |
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| 作者姓名： | 刘林青 马红明 李鹏 段子荷 李梦宇 邓芳明 |
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| 作者单位： | 国网河北省电力有限公司营销服务中心,国网河北省电力有限公司营销服务中心,国网河北省电力有限公司,国网河北省电力有限公司营销服务中心,国网河北省电力有限公司营销服务中心,华东交通大学 电气与自动化工程学院 |
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| 基金项目： | 国家自然科学基金资助项目(51767006)；江西省自然科学基金重点项目(20202ACBL214021)；江西省重点研发计划资助项目(20202BBGL73098)；江西省教育厅科学技术项目(GJJ190311) |
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| 摘 要： | 针对智能电能表在大规模使用后出现的寿命异常缩短，能耗异常增高问题。提出了一种基于边缘计算的智能电能表能耗与寿命优化方案。使用边缘服务器接收和上传智能电能表数据，在边缘端通过卷积神经网络(CNN)提取能耗与寿命的影响因子，采用K-means聚类算法预测用电量变化从而得到能耗与寿命优化模型。仿真结果表明，在基于边缘计算的能耗与寿命优化环境中，优化的1000个智能电能表的使用寿命提高了30%,总能耗降低了26%。为智能电能表长期稳定工作提供了一种研究方法。
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| 关 键 词： | 智能电能表 边缘计算 CNN K-means聚类算法 |
| 收稿时间： | 2020/11/3 0:00:00 |
| 修稿时间： | 2020/11/16 0:00:00 |
Energy consumption and life optimization method of smart meter based on edge computing |
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| liu lin qing,deng hong ming,li peng,duan zi he,limengyu and deng fangming.Energy consumption and life optimization method of smart meter based on edge computing[J].Electrical Measurement & Instrumentation,2023,60(5):173-179. |
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| Authors: | liu lin qing deng hong ming li peng duan zi he limengyu and deng fangming |
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| Affiliation: | Marketing service center of State Grid Hebei Electric Power Co., Ltd, Heibei,Marketing service center of State Grid Hebei Electric Power Co., Ltd, Heibei,State Grid Hebei Electric Power Co., Ltd, Heibei 050022, China,Marketing service center of State Grid Hebei Electric Power Co., Ltd, Heibe,State Grid Hebei Electric Power Co., Ltd, Heibei,East China Jiaotong University |
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| Abstract: | Aiming at the problems of life shortening and energy consumption increasing of smart meters after large-scale use. This paper presents an energy consumption and life optimization scheme of smart meters based on edge computing. The edge server is used to receive and upload the data of smart meters, and the influence factors of energy consumption and life are extracted by convolutional neural network (CNN) at the edge end, and K-means clustering algorithm is used to predict the change of power consumption, so as to obtain the energy consumption and life optimization model. The simulation results show that in the energy consumption and life optimization environment based on edge computing, the service life of 1000 smart meters is increased by 73%, and the total energy consumption is reduced by 26%. It provides a research method for long-term stable operation of smart meters. |
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| Keywords: | smart meter edge computing CNN K-means clustering algorithm |
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