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The Net-Zero Energy Residential Test Facility (NZERTF), at the National Institute of Standards and Technology (NIST) in Gaithersburg, Maryland, is a research house that is comparable in size and aesthetic to the houses in the greater Washington DC metro area. The purpose of the NZERTF is to demonstrate the feasibility of achieving net zero energy over the course a year (i.e., energy generated using photovoltaic modules and a solar hot water system equals energy consumed). The lifestyle choices of the occupants can have a substantial effect on the overall energy consumption of the house. As a laboratory facility, a methodology was needed to simulate the occupants' behavior. The occupancy in the NZERTF is emulated by a virtual family of four whose behavior and activities are based on recommendations published by the U.S. Department of Energy. In order to attempt to realistically emulate the daily activities of the virtual family it is necessary to replicate their occupancy profiles, water usage, lighting usage, miscellaneous electric plug loads, cooking loads, appliances loads, and sensible and latent loads. This paper discusses the methodology, strategy, and hardware behind emulating the occupancy in the NZERTF. KeywordsNet zero energy house; net zero energy residential test facility; occupancy profile; occupancy schedule; occupancy emulation; daily occupancy emulation schedule iv

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In order to develop effective control optimization strategies to manage residential electricity consumption in a smart grid environment, predictive algorithms are needed that are simple to implement, minimize custom configuration, and provide sufficient accuracy to enable meaningful control decisions. Two of the largest electrical loads in a typical residence are heating and airconditioning. A self-learning algorithm for predicting indoor temperature changes is derived using a first-order lumped capacitance technique. The algorithm is formulated in such a way that key design details such as window size and configuration, thermal insulation, and airtightness that effect heat loss and solar heat gain are combined into effective parameters that can be learned from observation. This eliminates the need for custom configuration for each residence. Using experimental data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF), it was demonstrated that an effective overall heat transfer coefficient and thermal time constant for the house can be learned from a single nighttime temperature decay test. It was also demonstrated that an effective solar heat gain coefficient can be learned without knowledge of the window area and orientation by application of a self-learning, sliding-window algorithm that accounts for seasonal variations and daily weather fluctuations. The resulting algorithm is shown to be able to predict indoor temperatures for a one-day time horizon using a solar irradiance and outdoor temperature forecast, and control decisions for operating a heat pump.