Residential and commercial buildings account for more than 74% of total annual electricity consumption in the United States. Studies have shown that occupants' awareness of their behaviors in consuming electricity encourages them to change their unsustainable behaviors and improves the sustainable ones. As behaviors impact the ways that daily activities are performed, in order to develop a personalized appliance level model of an occupant's behavior, precise activity recognition is required. In this paper, we introduce a novel framework to allocate personalized appliance-level disaggregated electricity consumption to daily activities. In our framework, using ontology based approach, the input appliance usage data is first separated into categories of non-overlapping activity events. The separated data sets are then segmented to detect activity segments, which are next mapped into activity classes using a trained classification model. To evaluate the performance of our presented framework, an experimental validation was carried out in three test bed apartment units. Results of validation showed a total F-measure value of 0.97 for segmentation and an average accuracy of 93.41% for activity recognition. Following the activity recognition, the approximate electricity consumption associated with the recognized activities was estimated and the results of each test bed unit were compared with the others.

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In recent years, technological advances have substantially extended the capabilities of automation systems in buildings. Despite the achieved advances, automation systems have not been widely adopted by building occupants. This paper presents ourinvestigations onautomation preferences of occupants for the control of lighting systems and appliancesin residential buildings. A survey was carried out to determinehow preferences for level of automation vary by contexts as well asindividuals' personalities and demographic characteristics. The contexts investigated in this study include rescheduling an energy consuming activity, activity-based appliance state control,and lighting control. The collected data from 250 respondents wereanalyzed using Generalized Linear Mixed Models. Based on the results,an automation level with higher user participation is more preferredfor rescheduling an activity. For control 24 ofactivity-based appliance states and lighting,levels of automation with lower user participation are more preferred. Our findings also indicate that income and education levels and alsopersonality traits of 26 agreeableness, neuroticism and openness to experience affect the preference of particular automation 27 levels over the others. Findings from this study can be used in designing user-centered automation 28 systems that lead to potentially more satisfying operation and hence, couldenhance automation 29 acceptability.

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