Stochastic simulation of uncertain input parameter in urban building energy modelling

Urban building energy modelling (UBEM) is a widely utilized technique for identifying effective strategies for a sustainable building stock. The accurate estimation of input data is of critical importance for UBEM, especially when complete and reliable data are unavailable. Additional challenges arise when building data are available but have been collected for purposes other than energy modelling. This can have a tremendous effect on the accuracy of UBEM. Since UBEM addresses fundamental questions in the transition to a green and CO2-neutral building stock, the assessment of the impact of uncertainties on the results is crucial.

Previous research has highlighted the need to address uncertainties in input data and the influence of such uncertainties on UBEM outcomes. The objective of this study is to utilize input data uncertainties to determine realistic ranges of expected energy demand. This study focuses on reliably estimating time-varying probability distributions and curves of the expected energy demand based on uncertainty-based scenarios. A stochastic simulation algorithm (SSA) is utilized to generate a representative and reliable set of uncertainty-based scenarios. Each such scenario is simulated with unique uncertainty-based parameter variations using the City Energy Analyst software. Finally, all scenarios are summarized by generating a mean energy curve and time-dependent standard deviation according to different probabilities of occurrence. This approach is presented using a case study in Germany.

The methodology helps to overcome the challenge of dealing with uncertainties in UBEM by creating realistic ranges of possible simulation results. The significant advantage of this approach is that it incorporates input data uncertainties, presenting realistic ranges of expected energy demands. The possible uncertainty-based scenarios can now be included in subsequent steps after an urban building energy simulation, be it for the design of an energy supply, the impact of a refurbishment strategy or any other application.