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Buildings represent one of the most signiflcant sources of energy consumption in the United States and other countries in the world. One of the most signiflcant factors affecting buildings' energy performance is the behavior and actions of their occupants. Monitoring, understanding, and decoding occupant's activities are fundamental to identify energy waste and for proposing strategies to reduce excessive energy consumption in buildings. In this paper, we present an approach for automatic detection and proactive monitoring of energy waste caused by occupants' behaviors. We flrst introduce a mathematical formalism to model states and trajectories arising in buildings in the context of energy consumption by occupants. Then, we present a set of easy to implement algorithms that used sensing information to detect wasteful states and trajectories. We also describe and implement a prototype of a non-invasive, sensor network consisting of inexpensive temperature, light, and distance sensors, as well as electricity consumption plug monitors that capture data related to occupancy behaviors in energy consumption. By combining occupancy counts, sensing information, and energy expenditures in different regions of a building, we can estimate how occupancy behavior is affecting energy use in a non-invasive way. Our ideas are tested experimentally in a study case in a residential building. [ABSTRACT FROM AUTHOR]