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 recent 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.