A Comparative Analysis of Different Future Weather Data for Building Energy Performance Simulation

Abstract

The building energy performance pattern is predicted to be shifted in the future due to climate change. To analyze this phenomenon, there is an urgent need for reliable and robust future weather datasets. Several ways for estimating future climate projection and creating weather files exist. This paper attempts to comparatively analyze three tools for generating future weather datasets based on statistical downscaling (WeatherShift, Meteonorm, and CCWorldWeatherGen) with one based on dynamical downscaling (a future-typical meteorological year, created using a high-quality reginal climate model). Four weather datasets for the city of Rome are generated and applied to the energy simulation of a mono family house and an apartment block as representative building types of Italian residential building stock. The results show that morphed weather files have a relatively similar operation in predicting the future comfort and energy performance of the buildings. In addition, discrepancy between them and the dynamical downscaled weather file is revealed. The analysis shows that this comes not only from using different approaches for creating future weather datasets but also by the building type. Therefore, for finding climate resilient solutions for buildings, care should be taken in using different methods for developing future weather datasets, and regional and localized analysis becomes vital.

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

“…However, as also previously mentioned, the projections for the future climate change are considerably higher for the weather file developed with Meteonorm generator using statistical downscaling methods compared to the RegCM_41-60 and the USWD_41-60, the generation of which is based on dynamical downscaling approaches. The lower dispersion of data in cases where future weather files have been generated with dynamical downscaling methods compared to datasets derived from statistical downscaling methods has also been mentioned in previous scientific studies of Tootkaboni et al [27] and Berardi et al [23]. The higher differences produced by the statistical downscaling methods compared to the dynamical approaches are strongly associated with the different algorithms and spatial representativeness followed at each case.…”

“…The various geomorphological characteristics of an area have an important role in defining the local climate. Yet, considering that the GCMs provide climate information at a rather coarse spatial resolution of 150-300 km [27] and therefore cannot represent the heterogeneity of climate variability, they must be temporally and spatially downscaled to be compatible with building energy performance simulation tools, requiring input information at finer spatial scale and at a temporal resolution of 1 h. To date, there are two main approaches to downscale GCMs: statistical and dynamical downscaling. These methods can provide high-resolution climatic information, as they model the interactions between large-scale atmospheric processes and local-scale characteristics.…”

Introducing the Built Environment in a Changing Climate: Interactions, Challenges, and Perspectives

“…To answer the first question, in [30], the authors focus on the need for reliable and robust future weather estimators and weather files generators by comparing three commonly used tools based on statistical downscaling (WeatherShift, Meteonorm, and CCWorld-WeatherGen) against the typical meteorological year prediction obtained by high-quality regional climate modelling and dynamic downscaling. The energy consumption of a residential house and an apartment in Rome (Italy) was simulated by forcing the boundary conditions in accordance with the four generated future datasets.…”

Dynamic Evaluation of the Effects of Climate Change on the Energy Renovation of a School in a Mediterranean Climate

“…Long-term changes in external climate are expected to substantially impact the building energy requirements [5], and therefore, the building energy performance model will be shifted in the future [6]. Due to climate change, buildings may suffer from overheating problems and may no longer guarantee the necessary comfort and productivity, especially in warm and hot climates.…”