Assessing the Ability of WRF‐BEP + BEM in Reproducing the Wintertime Building Energy Consumption of an Italian Alpine City

Abstract

The prediction of energy demand from buildings represents a key information for optimizing the energy supply, and for planning the use of renewable energy sources, as well as the adoption of strategies to mitigate undesired urbanization effects, such as higher temperatures associated with urban heat islands (Oke et al., 2017). Indeed, in 2018, buildings accounted for 26% of the total final energy use in the EU, and for about 16% of energy-related greenhouse gas emissions. In particular, 64% of the final energy consumption in the residential sector was due to space heating, with renewables accounting for about a quarter (European Commission, 2020). In this regard, several works highlighted that significant reductions in carbon dioxide (CO 2 ) emissions can be achieved from the optimization of the energy use (Pérez-Lombard et al., 2008). Accordingly, many modeling techniques have been developed to simulate energy consumption for space heating and cooling, both at the building and at the city scale. However, as reported in Swan and Ugursal ( 2009), these models may differ as to the level of detail required for the input data, for the time required for processing, and for the degree of complexity. Models such as the US Department of Energy's Energy-Plus (https://energyplus.net/) or TRNSYS (www.trnsys.com) operate at the building scale (Pappaccogli et al., 2018) and have been extensively applied to assess the profiles of energy demand of specific building types (e.g., Kazas et al., 2015;Moreci et al., 2016). Indeed, such models can reproduce quasi-real building features, such as homogeneous zones inside the building interiors, complex layered walls, glazing and shading systems, as well as solar and occupant passive heat gains. Nevertheless, they generally consider buildings as stand-alone entities, neglecting the interactions with the surrounding neighboring ones. This may result in significant errors in the simulation of the building energy demand, as shown by Han et al. (2017). For example, Pisello et al. ( 2012) report overestimates of the energy demand for space heating up to +22%, if the thermal interactions between surrounding neighboring buildings are neglected. In fact, energy consumption for both space heating and cooling is strongly influenced by the surrounding envi-Abstract This work aims at assessing the ability of the Weather Research and Forecasting model, coupled with a multilayer urban canopy scheme and a building energy model (BEP + BEM), to simulate the building energy consumption in the city of Bolzano (Italy) during wintertime. Estimates of the actual energy consumption, to be adopted as a benchmark for evaluating model results, are obtained from observed total annual values, combined with load curves derived from real-time measurements in various meteorological conditions and for different building types. The validation of numerical results highlights that the modeling set-up adopted is able to well reproduce the spatial distribution of the energy consumption in the urban area and its dependence ...

Persistent Wintertime Thermal Inversion in the City. The case of Madrid. Part I. Meteorology

“…As it will be shown below, these elements are very important and determine the evolution of the PBL structure over Madrid during persistent wintertime inversions. This work is presented in two parts: this first part focuses on meteorology, while the second part (Martilli et al 2021) analyzes the pollutant dispersion. IN section 2 the case study, available measurements and model set-ups are presented.…”

“…Extending the analysis to numerical studies for other cities in similar situations, we can conclude that mesoscale simulations of persistent wintertime inversions over cities performed by models with UCP are lacking. This is probably because, historically, mesoscale models with UCP have been used and evaluated for summer periods, aiming to understand the causes of urban overheating and develop tools that can be used to evaluate heat mitigation strategies, and little attention has been paid to winter situations, except Salamanca and Mahalov (2019), and Pappaccogli et al (2021).…”

Moderating AC Usage Can Reduce Thermal Disparity between Indoor and Outdoor Environments

“…This surge in demand will constitute approximately 30% of the total global building electricity consumption . Mesoscale meteorological models offer the capability to estimate cooling-related energy consumption by incorporating both building and urban climate characteristics into their modeling framework. ,,, Nevertheless, due to the scarcity of high-temporal and high-spatial-resolution energy consumption data, AC simulation models possess a distinct advantage in describing spatial and temporal energy consumption patterns. Furthermore, these models enable the exploration of AC regulation measures aimed at reducing and adjusting peak electricity demand burdens.…”

On the Calculation of Urban Morphological Parameters Using GIS: An Application to Italian Cities

“…Similarly, other authors have derived relevant flow and dispersion parameters related to urban morphology for parts of some northern European cities, such as London, Toulouse, Berlin (e.g., [18]) and for main Chinese and Indian cities (e.g., [19][20][21][22][23]). However, little attention has been paid to cities in southern European and Mediterranean regions (see for example [24][25][26]).…”