Improving predictions of operational energy performance through better estimates of small power consumption
2013-11-13T11:09:40Z (GMT) by
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.