Journal of Network and Computer Applications
Journal of Network and Computer Applications
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DOI：
10.1016/j.jnca.2017.09.001
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
Weishan Zhang;Gaowa Wulan;Jia Zhai;Liang Xu;Dehai Zhao;Xin Liu;Su Yang;Jiehan Zhou
中科院分区：
文献类型：
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作者：
Weishan Zhang;Gaowa Wulan;Jia Zhai;Liang Xu;Dehai Zhao;Xin Liu;Su Yang;Jiehan Zhou
智能管理能源消耗，实现绿色生活，对可持续发展至关重要。现有的方法并不能提供一个完整的解决方案，既可以智能监控用电量，又可以有效地对采集到的数据进行智能处理。本文提出了一种基于云的智能配电服务架构，采用Zigbee和树莓派设计智能配电箱（IEB），采用标准的MQTT （Message Queuing Telemetry Transport）协议，利用开源软件包将监控数据传输到后端云计算基础设施。IEB提供实时用电信息查询、用电监测、交换机远程控制等云服务。当前和历史数据存储在HBase中，并使用LSTM （Long Short Term Memory）进行分析。评估和实际使用表明，我们提出的解决方案在可用性和性能方面非常有效，并且基于深度学习的方法比基于经典SVR的方法和最新的XGBoost方法具有更好的预测精度。
Smart management of power consumption for green living is important for sustainable development. Existing approaches could not provide a complete solution for both smart monitoring of electricity consumption, and also intelligent processing of the collected data effectively. This paper presents a cloud-based intelligent power distribution service architecture, where an intelligent electricity box (IEB) is designed using Zigbee and Raspberry Pi, and a standard MQTT (Message Queuing Telemetry Transport) protocol is used to transfer monitored data to the backend Cloud computing infrastructure using open source software packages. The IEB provides cloud services of real-time electricity information checking, power consumption monitoring, and remote control of switches. The current and historical data are stored in HBase and analyzed using Long Short Term Memory (LSTM). Evaluations and practical usage show that our proposed solution is very efficient in terms of availability, performance, and the deep learning based approach has better prediction accuracy than that of both classical SVR based approach and the latest XGBoost approach.