Future Weather Data for Dynamic Building Energy Simulations: Overview of Available Data and Presentation of Newly Derived Data for Belgium

Abstract

As buildings have a relatively long life span, it is important to consider climate change in energy performance modelling. Good quality weather data are needed to obtain accurate results. This chapter discusses widely used methods to predict future weather data (dynamical downscaling, stochastic weather generators and morphing) and provides an overview of available weather datasets (multi-year, typical years, extreme years and representative years) for building simulations. A Flemish office building is used for a comparative analysis of the estimated heating and cooling load making use of one-year weather files (typical and extreme future climate conditions) derived from a recently developed convection-permitting climate model for Belgium. Climate models and weather generators are identified as the most preferred for the estimation of the average energy consumption and thermal comfort in average and extreme situations. Climate models have the advantage to better represent extreme weather events and climate differences due to territorial settings, while weather generators can generate multiple climate realizations. A combination of a typical year with an extreme cold and extreme warm year was found to result in an overall good representation of the energy need for heating and cooling in average and extreme weather conditions. Further, the influence of the methodological choices to extract one-year weather files (typical or extreme years) from the 30-year climate data is highlighted as different results were obtained when different meteorological variables were considered for the creation of the one-year files.

Estimation, analysis and mapping of electricity consumption of a regional building stock in a temperate climate in Europe

“…The cooling and heating (EC) of buildings depends not only on temperature but also on building characteristics, household composition, consumer habit, electrical equipment, etc. [22,24,57,58]. Data scarcity and difficulties to measure some of these parameters are the principal challenges related to the development of the models.…”

Climate Change Effects on Belgian Households: A Case Study of a Nearly Zero Energy Building

“…The methodology is applied to a Belgian residential reference building using future climate change weather files for the years 2050 and 2100. Research focusing on future climate scenarios using building performance simulations in the Belgian context is rare [9]. At the same time, Belgium faces a remarkable increase in both extreme temperatures and air conditioning and refrigeration systems sales (see Figure 1) [10,11].…”

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

“…Global climate models are complicated numerical models that simulate the state and evolution of the atmosphere, including the atmospheric circulation and energy exchanges in terms of radiation, heat, and moisture. They simulate the processes related to cloud formation and precipitation and take into account the interaction with the ocean and the land [32]. To check if GCMs can simulate the evolution of the climate systems, they are validated against past climate conditions [33].…”

“…The stochastic weather generation method has the advantage of enabling the integration of the distribution used for the climate change signal. In addition, it is accountable for potential changes in weather patterns and climate variability [32]. However, what appears to be a limitation of this method is the need for a large amount of data to train the model, since distributions for generating future data are based on the baseline data given to the model [35].…”