An intelligent power distribution service architecture using cloud computing and deep learning techniques. (1st February 2018)

• Record Type: Journal Article
• Authors: Zhang, Weishan; Wulan, Gaowa; Zhai, Jia; Xu, Liang; Zhao, Dehai; Liu, Xin; Yang, Su; Zhou, Jiehan
• Is Part Of: Journal of network and computer applications. Volume 103(2018)
• Page Start: 239
• Page End: 248
• Publication Date: 2018-02-01
• Subjects: LSTM -- MQTT -- Power distribution -- SVR -- Prediction -- Cloud computing -- XGBoost; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2017.09.001
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: 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.