The overall goal of this thesis is to provide a high level framework and motivation for intelligent building development by using data available in typical base building systems and finding synergies between these data streams to evaluate questions progressing beyond the traditional building operational metrics. This is done by exploiting a highly underutilized data point in buildings, the occupants, and relating this to the business objectives of tenants, property managers, and other key stakeholders to develop more interesting and valuable key performance indicators.

This framework is then deployed in a living laboratory, commercial office environment in Boulder, CO, to evaluate a portion of the developed metrics using commercially available building systems. These metrics mainly focus on space utilization and energy/power characteristics, but evaluates the effectiveness of grouping them according to the spatial hierarchy of the building and the internal business groups of the tenant.

The most significant contribution of this thesis is to evaluate two means by which energy consumption characteristics can be better evaluated with respect to the actual occupancy and spatial utilization patterns of the building. Since commercial office spaces are designed to be used by people, these methods consider the energy consumption in reference to the actual building occupancy, and are therefore referred to as "Occupancy Normalized Energy". These metrics are a step beyond evaluating the efficiency of a building by looking solely at energy consumption, but provide a basis for evaluating the effective usage of commercial offices.