Smart home energy management systems based on non-intrusive load monitoring

The concept of smart home has attracted considerable attention in recent years, and energy management is one of its key component. This attributes to the growing concern in environmental protection and energy conservation, as well as the demands for big data collection from utility companies and policy makers. Current solutions often approach this problem by either the centralized non-intrusive load monitoring (NILM) or the decentralized smart controls but seldom both, rendering them impractical to some extent. Therefore, in this paper, we propose a novel framework of smart home energy management systems incorporating both approaches, so that accurate power consumption monitoring and intuitive interaction with the home appliances are simultaneously achieved. The smart components directly control the appliances, while the central controller coordinates the data collection and communication. The key feature is the capability of automatically mapping the appliances to their corresponding sockets, reducing the necessity for manual initial setup. Numerical simulations prove the accuracy and efficiency of the framework. We believe that our systems, if widely deployed, can benefit not only individual households by saving energy bills and simplifying life but also society by the big data generated.

I. Introduction

Increasing emphasis on environmental protection and smarter energy uses nowadays calls for better energy management systems. For households this typically involves managing the power consumption of all appliances and controlling their operation states. Such smart home systems provide users with not only a more concrete illustration of their energy consumption behaviors but also the possibility to remotely or even automatically control their home appliances. These functions will eliminate the energy wastes and the potential risks introduced by the appliances running overtime. Moreover, utility companies can exploit the big data collected by the systems for better load forecasting and policy making, which has been a key motivation to push forward the deployment of smart meters [1].