Home Energy Management System Using NILM and Low-Cost HAN

Home energy management systems （HEMs） are used to provide comfortable life for consumers as well as to save energy. An essential component of HEMs is a home area network （HAN） that is used to remotely control the electric devices at homes and buildings. Although HAN prices have dropped in ~ecent years but they are still expensive enough to prohibit a mass scale deployments. In this paper, a very low cost alternative to the expensive HANs is presented. We have applied a combination of non-intrusive load monitoring （NILM） and very low cost one-way HAN to develop a HEM. By using NILM and machine learning algorithms we find the status of devices and their energy consumption from a central meter and communicate with devices through the one-way HAN. The evaluations show that the proposed machine learning algorithm for NILM achieves up to 99% accuracy in certain cases. On the other hand our radio frequency （RF）-based one-way HAN achieves a range of 80 feet in all settings.