Evaluating the Combined Effect of Climate Change and Urban Microclimate on Buildings’ Heating and Cooling Energy Demand in a Mediterranean City

Abstract

Climate change has a major impact on the urban built environment, both with respect to the heating and cooling energy requirements, but also regarding the higher probability of confronting extreme events such as heatwaves. In parallel, the ongoing urbanization, the urban microclimate and the formation of the urban heat island effect, compounding the ongoing climate change, is also a considerable determinant of the building’s energy behavior and the outdoor thermal environment. To evaluate the magnitude of the complex phenomenon, the current research investigates the effect of climate change and urban heat island on heating and cooling energy needs of an urban building unit in Thessaloniki, Greece. The study comparatively evaluates different tools for the generation of future weather datasets, considering both statistical and dynamical downscaling methods, with the latter involving the use of a regional climate model. Based on the output of the regional climate model, another future weather dataset is created, considering not only the general climatic conditions, but also the microclimatic parameters of the examined case study area, under the future climate projections. The generated future weather datasets are then used as an input parameter in the dynamic energy performance simulations with EnergyPlus. For all examined weather datasets, the simulation results show a decrease of the heating energy use, an effect that is strongly counterbalanced by the rise of the cooling energy demand. The obtained simulation results also reveal the contribution of the urban warming of the ongoing climate change, demonstrating the need to perform a holistic analysis for the buildings’ energy needs under future climate conditions.

Analysis of long‐term trends and variations in extreme high air temperatures in May over Turkey and a record‐breaking heatwave event of May 2020

“…Heatwaves at the beginning of the HW season (May–September) and changes in the seasonality of extreme high temperatures may have affected agriculture production (e.g., Barriopedro et al ., 2011; Kornhuber et al ., 2020), increased a heat‐related mortality and other HW related health risks (e.g., Hajat et al ., 2002; Sheridan and Lin, 2014; Muthers et al ., 2017), risk of wildfires (Turco et al ., 2016; Ruffault et al ., 2020), droughts (e.g., Miralles et al ., 2019; Russo et al ., 2019), and energy demand for air conditioning (Añel et al ., 2017; Tsoka et al ., 2021).…”

Comparison of building energy performance in three urban sites using field measurements and modelling in Kayseri, Turkiye

“…Predicting the impact of the urban form on the local microclimate, which affects not only outdoor thermal comfort but also building energy use, demand, and building thermal resilience, is one of the key issues throughout the urban design process. The analysis of the energy performance of buildings and their effects on the environment must increasingly consider the local climatic conditions, according to previous empirical and theoretical research [2][3][4]. Many researchers have focused on ways to measure the effect of urban microclimates on energy usage during the last 20 years.…”

“…The ENVI-met model is a prognostic microclimate model that has been specifically developed to simulate the complex interactions among surfaces, plants, and air within an urban setting [7]. The use of this method is prevalent in the estimation and evaluation of outdoor thermal comfort [8], [9] , as well as in the examination of the influence of the urban microclimate on building energy consumption [2], [10].…”

Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need

“…The simulations are often performed for decision making in the design phase, and given the high number of variables to consider, a huge number of simulations is usually necessary before identifying the most suitable and efficient solutions. Even though a single simulation is relatively fast (taking from a few seconds up to a couple of hours depending on the modelled building and the adopted computer), each simulation requires an operator who inserts, analyses, assesses, makes decisions, leading to a time consuming process that needs constant human supervision to avoid unintended effects [8][9][10].…”