Research on Energy Management System Integration and Energy Saving Optimization

DOI

10.2991/gcmce-17.2017.22How to use a DOI?

Keywords

Energy management, Wireless sensor networ, Zigbee, Flume, Kafka, Storm, Redis, SVM, Spark.

Abstract

With the rapid development of modern society, an increasing number of people start to realize the importance of the electric power consumption of buildings. Based on this, the article explore the effective management method of energy consumption of buildings: First of all, through the DHT11(temperature and humidity sensors), on-site temperature and humidity data, together with the energy consumption data are collected and sent back to the linux file through the zigbee technology. The real-time data is saved to a linux file through executing Python program. Then applying the Flume+Kafka+Storm+Redis real-time analysis structure: As the producer of Kafka, Flume monitors whether the file has generated new data. As the consumer of Kafka, Storm cleans and organizes the data, monitors and predicts the energy consumption of the buildings. The data is analyzed periodically by using the Support Vector Machine (SVM) algorithm in spark MLlib and establish the forecasting model. At least, the data is stored in Redis and offline analysis is conducted periodically.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Qiang Zhao
AU  - Chenxiao Zhi
PY  - 2017/06
DA  - 2017/06
TI  - Research on Energy Management System Integration and Energy Saving Optimization
BT  - Proceedings of the 2017 Global Conference on Mechanics and Civil Engineering (GCMCE 2017)
PB  - Atlantis Press
SP  - 111
EP  - 114
SN  - 2352-5401
UR  - https://doi.org/10.2991/gcmce-17.2017.22
DO  - 10.2991/gcmce-17.2017.22
ID  - Zhao2017/06
ER  -