In this project saving energy is one of the biggest concerns nowadays in order to deal with lacking fossil fuels and climate change. One possible approach is using the Home Energy Management System (HEMS) to monitor and manage the electrical usage of the residential user. One useful feature of HEMS is the capability of detecting and identifying electrical appliances in order to assign these appliances to join many saving energy programs such as Time-Of-Use or Demand Response.
In this paper, we propose an appliance classification approach based on machine learning. Classifier algorithm owing to its simplicity, ease of implementation, and effectiveness. We also implement a HEMS prototype that includes many smart plug-in devices and an MQTT server. Machine learning enables the user to consume energy more efficiently with the prediction of consumption for the month. This can be done by analyzing the usage for the first week of the month. Also using MQTT user is able to see the daily and monthly usage and if the usage exceeds the monthly limit user is alerted.
- Conventional home electrical system
- Load switching without machine learning
- Less accurate load analysis
- Monitors all the loads combined
- Not possible to find out any power leakage
- Not possible to find a faulty load
- Machine Learning algorithm
- Wireless communication based on Protocol
- Less energy consuming
- Highly efficient power management
- Customer daily and monthly notification
- Alert message for monthly and daily limits exceeding
- Nodemcu based hardware implementation
- Continuous current, voltage, power, and energy unit monitoring
- Users can also control the appliances remotely through IoT using MQTT
- Current transformer
- Potential transformer
- Arduino IDE
- anaconda IDE
- Industrial appliance
- Home appliance
 Xheladini, Azra, Sertan Deniz Saygili, and Ferhat Dikbiyik. “An IoT-based smart exam application.” In Smart Technologies, IEEE EUROCON 2017-17th International Conference on, pp. 513-518. IEEE, 2017.
 Minoli, Daniel, Kazem Sohraby, and Benedict Occhiogrosso. “IoT security (IoTsec) mechanisms for e-health and ambient assisted living applications.” In Proceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, Systems, and Engineering Technologies, pp. 13-18. IEEE Press, 2017.
 Wang, Shulong, Yibin Hou, Fang Gao, and Xinrong Ji. “A novel IoT access architecture for the vehicle monitoring system.” In the Internet of Things (WF-IoT), 2016 IEEE 3rd World Forum on, pp. 639-642. IEEE, 2016.
 Biswas, Abdur Rahim, and Raffaele Giaffreda. “IoT and cloud convergence: Opportunities and challenges.” In 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 375-376. IEEE, 2014.
 Teja, P. Satya Ravi, V. Kushal, A. Sai Srikar, and K. Srinivasan. “Photosensitive security system for theft detection and control using GSM technology.” In Signal Processing And Communication Engineering Systems (SPACES), 2015 International Conference on, pp. 122-125. IEEE, 2015.
 O. Natu, “GSM Based Smart Street Light Monitoring,” IEEE, 2013
 I. A. C. L. Zeeshan Kaleem, “Smart and Energy Efficient LED Street Light Control,” IEEE.
 Shahzad, Gul, Heekwon Yang, Arbab Waheed Ahmad, and Chankil Lee. “Energy-efficient intelligent street lighting system using traffic adaptive control.” IEEE Sensors Journal 16, no. 13 (2016): 5397-5405.