Load monitoring techniques determine the appliances that are turned ON within a given period of time in a household or workplace. They can help occupants optimize their
power consumption behavior. Load monitoring is broadly classified as intrusive or nonintrusive. Intrusive load monitoring requires the attachment of individual sensors to each
appliance being monitored, thus resulting in large initial expenses. However, in non-intrusive load monitoring (NILM), the objective is to determine which appliances are ON and their respective power consumptions through a single supply entry point. Thus, with its cost effectiveness, there has been an escalation in the demand for efficient and effective NILM methods for residential appliance identification. Even though there are many different NILM methods proposed in the literature, most of them estimate the currently turned ON appliances only using electrical measurements such as current and/or voltage, active power, and reactive power. However, certain appliances have a higher chance of being ON at certain times of day. For example, a toaster would often get used in the morning, and likely never overnight. Therefore, there is high potential to utilize such time-of-day information in addition to electrical measurements to develop more accurate NILM methods. In this research, we extend a recent NILM method to utilize prior time-of-day information of various appliances. Incorporating this information into NILM leads to both improvements in load monitoring accuracy as well as reduction in execution time.