The 2015 Paris Agreement has the central aim to strengthen the global response to the threat of climate change by keeping global temperature rise in this century well below 2 degrees Celsius above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5 degrees Celsius. To reach this ambitious goal, two central strategies have to be implemented in most European countries: (i) enhancing energy efficiency (EE) and (ii) decarbonising remaining energy supply and demand, in particular, by large penetration of renewable energy sources (RES). Scenarios with different focuses and assumptions have been developed to map this development until 2050. While these scenarios represent a major step forward beyond previous modelling approaches, most of them are not considered new societal trends. newTRENDs aims to contribute to fill this gap progress by identifying relevant trends and improve their modelling based on recent empirical findings. In this context, the project newTRENDs is developing the analytical basis for a “2050 Energy Efficiency Vision” taking into account New Societal Trends in energy demand modelling.
This report discusses the universal trend cluster of digitalisation. Digitalisation has fundamentally transformed various aspects of our lives and the economy. This report examines how this transformation impacts the past and future energy demand of the tertiary sector, more specifically of the commercial, buildings and data centre sub-sectors. These changes in energy demand shall consequently be implemented in the demand simulation model FORECAST.
The aim of this report is to document the methodological enhancements of the FORECAST simulation framework and the added input data in order to better model these new trends in its tertiary sector module. For testing the enhancement, small and simple scenarios were defined that are suitable to
perform plausibility checks in the relevant parts of the model. These preliminary scenarios also highlight the order of magnitude of effects and potential countereffects, which partly balance each other out. However, final conclusions should be drawn from more specific case studies, ideally based on empirically validated input parameters.
With these new modules of FORECAST, the tertiary module of the forecast simulation framework can be significantly improved and is ready to model the new trends and their impact on the energy demand of buildings.
