Plug loads—traditionally viewed as behaviorally motivated and beyond the control of designers and operations—are now seen as an integral part of achieving low-energy building targets. Higher education institutions are increasingly recognizing the environmental impacts of campus facilities through holistic approaches to energy savings including energy efficient design and occupant engagement. Residence halls are a compelling example because students bring large numbers of electronics to their rooms and have unlimited access to power for an all-inclusive room rate and resource usage competitions and campaigns are commonplace. However, limited research exists on residence halls plug loads. This dissertation asked the following of residence halls: (1) What are the measured plug loads and how do they compare with design estimates? (2) What role do building design characteristics play in plug loads? (3) What are the specific occupant behaviors that could influence future design? (4) How can plug loads be better understood in terms of behavior, design, and operations? To answer these questions, a sequential mixed methods study included field measurements and student surveys in six residence halls on three Oregon campuses followed by 24 interviews with designers, operators, and students. Findings suggest that plug loads in occupied residence halls are higher and usage profiles differ from design predictions. Results do not show significant correlations between design characteristics and plug loads but suggest that some room/suite level features may play a somewhat stronger role. Survey responses indicated that students are doing more with fewer smart devices, which suggests opportunities for students sharing energy intensive devices. Lighting emerged as both a practical and a social consideration. Finally, the data revealed “balance of power” as a coherent process that explicates the relationships between design, operations, and behavior. Designers have the power to recommend plug load strategies and technologies but are limited by costs, maintenance, and political concerns; operations personnel have the power to impose limits on student power usage but are often reluctant to interfere with the overall living experience; and students have the power to use plug load electricity with few restrictions. This suggests that the balance may be skewed toward student behavior.

This Engineering Doctorate aims to understand the factors that generate variability in small power consumption in commercial office buildings in order to generate more representative, building specific estimates of energy consumption. Current energy modelling practices in England are heavily focussed on simplified calculations for compliance with Building Regulations, which exclude numerous sources of energy use such as small power. When considered, estimates of small power consumption are often based on historic benchmarks, which fail to capture the significant variability of this end-use, as well as the dynamic nature of office environments. Six interrelated studies are presented in this thesis resulting in three contributions to existing theory and practice. The first contribution consists of new monitored data of energy consumption and power demand profiles for individual small power equipment in use in contemporary office buildings. These were used to inform a critical review of existing benchmarks widely used by designers in the UK. In addition, monthly and annual small power consumption data for different tenants occupying similar buildings demonstrated variations of up to 73%. The second contribution consists of a cross-disciplinary investigation into the factors influencing small power consumption. A study based on the Theory of Planned Behaviour demonstrated that perceived behavioural control may account for 17% of the variation in electricity use by different tenants. A subsequent monitoring study at the equipment level identified that user attitudes and actions may have a greater impact on variations in energy consumption than job requirements or computer specification alone. The third contribution consists of two predictive models for estimating small power demand and energy consumption in office buildings. Outputs from both models were validated and demonstrated a good correlation between predictions and monitored data. This research also led to the development and publication of industry guidance on how to stimate operational energy use at the design stage.