Ever increasing demand for electricity in the residential sector has made providers implement demand management strategies to curtail residential electricity consumption during peak demand events. With time, customer satisfaction of such programs dropped, resulting in high dropout rates making utilities rethink about the success of such strategies. The future success of how well these strategies could be implemented is highly dependent on how well the providers understand the effect the strategies have on the customers. This in turn, requires them to study occupant and appliance behavior of each household to have a much better understanding of the problem and this information is not readily available. In this research, a discrete event simulation model is proposed (a Non-Homogenous Markov process), that will help simulate appliance and occupant level demand patterns for residential customers in order to allow for the study of consumer-friendly demand response strategies. To develop a valid model, both occupant behavior patterns and appliance level patterns have been combined to form a data-driven simulation process. The model accounts for various factors like climate, day of week, time of day, number of occupants and nature of the appliances used. The model is validated against real hourly smart meter data and the American Time Use Survey using Simio, a simulation package. Validation was conducted in two stages namely a statistical validation stage, where levels of confidence were calculated for the proposed model during different times of day and a more industry-friendly non-parametric stage, where modern clustering techniques were used to gauge how well the proposed model helps simulate the real-world data
