Paper Info
Title
An intelligent power distribution service architecture using cloud computing and deep learning techniques.
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.
Year
DOI
Venue
2018
10.1016/j.jnca.2017.09.001
Journal of Network and Computer Applications
Keywords
Field
DocType
LSTM,MQTT,Power distribution,SVR,Prediction,Cloud computing,XGBoost
Computer science,Computer network,Real-time computing,Message queue,Artificial intelligence,Deep learning,MQTT,Remote control,Electricity,Sustainable development,Service-oriented architecture,Cloud computing,Embedded system
Journal
Volume
Issue
ISSN
103
C
1084-8045
Citations 
PageRank 
References 
0
0.34
11
Authors
8
Name
Order
Citations
PageRank
Weishan Zhang139652.57
Gaowa Wulan200.34
Jia Zhai3186.83
Liang Xu45714.47
Dehai Zhao5273.72
Xin Liu68212.27
Su Yang711014.58
Jiehan Zhou822628.61