Home energy optimization is increasing in research interest as smart technologies in appliances and other home devices are increasing in popularity, particularly as manufacturers move to produce appliances and devices which work in conjunction with the Internet. Home energy optimization has the potential to reduce energy consumption through “smart energy management” of appliances. Information and communications technologies (ICTs) help achieve energy savings with the goal of reducing greenhouse gas emissions and attaining effective environmental protection in several contexts including electricity generation and distribution. This “smart energy management” is utilized at the residential customer level through “smart homes.” This paper compares two artificial neural networks (ANN) used to support home energy management (HEM) systems based on Bluetooth low energy, called BluHEMS. The purpose of the algorithms is to optimize energy use in a typical residential home. The first ANN uses the Levenberg-Marquardt algorithm and the second uses the Levenberg-Marquardt algorithm enhanced by a second order correction known as geodesic acceleration.