A smart home energy management system using IoT and big data analytics approach

• Published in: IEEE transactions on consumer electronics Vol. 63; no. 4; pp. 426 - 434
• Main Authors: Al-Ali, A. R., Zualkernan, Imran A., Rashid, Mohammed, Gupta, Ragini, Alikarar, Mazin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Air conditioners; Air conditioning; Analytics; Big Data; Business Intelligence; Case studies; Cost analysis; Data analysis; Data Analytics; Data management; Demand; Electricity consumption; Electronic devices; Energy conservation; Energy consumption; Energy costs; Energy management; Energy Management System; HVAC; Information management; Intelligence (information); Internet of Things; IP (Internet Protocol); Monitoring; MQTT; Residential areas; Residential energy; Sensors; Servers; Smart buildings; Smart homes; Software packages; System on Chip; Ventilation; Wireless communications; Wireless networks; Wireless sensor networks
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2017.015014

Increasing cost and demand of energy has led many organizations to find smart ways for monitoring, controlling and saving energy. A smart Energy Management System (EMS) can contribute towards cutting the costs while still meeting energy demand. The emerging technologies of Internet of Things (IoT) and Big Data can be utilized to better manage energy consumption in residential, commercial, and industrial sectors. This paper presents an Energy Management System (EMS) for smart homes. In this system, each home device is interfaced with a data acquisition module that is an IoT object with a unique IP address resulting in a large mesh wireless network of devices. The data acquisition System on Chip (SoC) module collects energy consumption data from each device of each smart home and transmits the data to a centralized server for further processing and analysis. This information from all residential areas accumulates in the utility's server as Big Data. The proposed EMS utilizes off-the-shelf Business Intelligence (BI) and Big Data analytics software packages to better manage energy consumption and to meet consumer demand. Since air conditioning contributes to 60% of electricity consumption in Arab Gulf countries, HVAC (Heating, Ventilation and Air Conditioning) Units have been taken as a case study to validate the proposed system. A prototype was built and tested in the lab to mimic small residential area HVAC systems1.