MRA as a Scale-Adaptive Driver for WUDAPT: An Example from Energy-related Applications

For the WUDAPT initiative to realize its full impact across  the modelling and end-user communities it is important to build the capacity of making the most out of the detailed and complete databases that modern technologies  enable us to acquire nowadays. Multi-Resolution Analysis (MRA) has illustrated its potential and capacity for scale-adaptive representations while retaining the capacity to retrieve the most detailed information when needed – a unique feature that can also lead to finding distinctive signatures of cities and other entities (e.g. Mouzourides et al, 2013, Ching et al., 2014, Mouzourides et al 2015). In this paper, we illustrate the MRA potential for WUDAPT in urban energy applications and in particular to building- energy demands, linking to the building-modified energy balance and the corresponding Urban Canopy parameterizations (UCPs). Specifically, we use a dynamic high-resolution dataset for the energy demands of Westminster borough within London City to illustrate how the urban scale dataset can be analyzed using the MRA to provide insights into the urban- and sub-urban (e.g. district) scale energy demand. We analyze full-day (24-hr) evolutions of the urban-scale energy demands for heating and cooling over different seasons for the entire building stock of Westminster City; our dataset includes as well high-resolution information on the building density and height, the population and the employment. By introducing the scale-adaptive approach, our analysis provides suggestions for e.g. scale-aware energy peaks identification and localization. Such capacity is valuable in order to link also large-scale weather or climate change models (and such aspects including connections to urban heat Island and urban induced ventilation) with the associated urban energy demand and its forecasting on a scale dependent bases.