Smart Home/Office Energy Management based on Individual Data Analysis through IoT Networks

Sustainable use of energy requires to achieve optimal energy utilization in smart grid systems. It is possible by empowering the Internet of Things (IoT) based Wireless connectivity through real-time energy monitoring and analyses of power consumption patterns. Modeling optimal energy utilization considering multi-user behaviors is particularly challenging in such context. To address the challenge of one-to-one-mapping of energy disaggregation in device-sharing environments by multiple co-existing users, a new method based on data-driven machine learning (e.g., individual energy usage pattern analysis) is proposed in this paper that aims to accurately match the energy consumption of electrical appliances with specific users. In particular, the machine learning model with the best performance is selected for real-time energy/power disaggregation on the local server (i.e., small-scale home/office) to ensure comparable or better performance with state-of-the-art disaggregation algorithms. In addition, energy usage patterns and individual power consumption data are analyzed comprehensively to match overall energy consumption and label datasets by events. Distributed learning is also discussed to exploit other local servers' datasets for better disaggregation through IoT networks. The effectiveness of the proposed method is verified by using simulated datasets in a motivating scenario.