Making energy simulation easier for future climate – Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)

Dr. Vahid M. Nik at Lund University in Sweden carried out a study, developing a new approach in impact assessment of climate change on buildings and their energy performance. His work is now published in the peer-reviewed journal, Applied Energy.

Impact assessment of climate change is performed using the statistically or dynamically downscaled future weather data from global climate models. Dynamical downscaling, using regional climate models (RCMs), provides projections with greater detail and more accurate representation of localized extreme events. The need for considering long time periods – e.g. 30 years – and several climate scenarios – due to important climate uncertainties – make the impact assessment challenging.

In this work, the researcher creates three sets of 1-year weather data out of RCMs for each 30-year period based on the distribution of dry bulb temperature. These three data sets represent the typical and extreme weather conditions in the 30-year period and they include; typical downscaled year TDY, extreme cold year ECY and extreme warm year EWY. The representative weather data set are synthesized considering single – 30 years of weather data in each period – or multiple climate scenarios – N×30 years of weather data in each period, which N is the number of climate scenarios.

The main motivation for creating such weather data sets was to decrease the calculation load while keeping a high accuracy in estimating the variations in hourly time scale and not neglecting future climate uncertainties. In Applying the method for synthesizing weather data and performing impact assessment, six climate scenarios in Geneva and two in Stockholm was used and the energy performance of an office building in Geneva and the residential building stock in Stockholm was investigated.

In the case of simulating the building stock using the original weather data sets, 153 buildings were simulated for 30 years and two climate scenarios, resulting in 9180 simulation years per period. Using the synthesized weather data, it decreases the number of simulations to 459 per period, which is 20 times less. For the Geneva case, where 6 climate scenarios were considered – multiple climate scenarios –, the suggested approach decreases the number of simulations from 180 per period to 3.

He then compared the obtained results of the energy simulation with the original RCM weather data and the synthesized one, where he refers the combination of TDY, ECY and EWY as Triple. The distribution of energy simulation results for using the TDY weather data set was found to be similar to the case when the building is simulated for all the years during the considered period, however extreme conditions were mostly neglected. The distribution was improved extensively when the results of the extreme weather data sets are also included – Triple case –, having distributions very similar to the results out of the original RCM weather data.

Dr. Vahid M. Nik proposed an easy-to-use method to decrease the number of simulations for the impact assessment of climate change in energy and building studies, the results obtain indicated using the synthesized data sets provides an accurate estimation of future conditions.  Based on the results, it is possible to use the synthesized weather data sets in energy simulations and produce reliable results, representing the cumulative energy distributions as well as the hourly variations.