Harnessing buildings’ operational diversity in a computational framework for high-resolution urban energy modeling
urban energy modeling, GIS, bottom-up engineering model, building stock, occupant behavior, building performance simulation
To achieve computational efficiency, efforts toward developing urban-scale energy modeling applications frequently rely on various domain simplifications. For instance, heat transfer phenomena are captured using reduced order models. As a consequence, specific aspects pertaining to the temporal dynamics of energy load patterns and their dependency on transient phenomena (e.g., weather conditions, inhabitants’ presence and actions) cannot be realistically represented. To address this circumstance, we have conceived, implemented, and documented a two-step urban energy modeling approach that combines cluster analysis and sampling techniques, full dynamic numeric simulation capability, and stochastic methods. The paper describes the suggested urban energy modeling approach and the embedded cluster analysis supported sampling methodology. More particularly we focus on the aspects of this approach that explicitly involve the representation of inhabitants in urban-scale energy modeling. In this regard, the potential to recover lost dynamic diversity (e.g., in computation of temporal load patterns) due to the deployed reductive sampling is explored. Parametric runs based on stochastic variations of underlying building use profiles facilitate the generation of highly realistic load patterns despite the small number of buildings selected to represent the simulation domain. We illustrate the utility of the proposed urban energy modeling approach to address queries concerning the energy efficiency potential of behaviorally effective instruments. The feasibility of the envisioned scenarios concerning inhabitants and their behavior (high-resolution temporal load prediction, assessment of behavioral variation) is presented in detail via specific instances of district-level energy modeling for the city of Vienna, Austria.
Tsinghua University Press
Neda Ghiassi, Farhang Tahmasebi, Ardeshir Mahdavi. Harnessing buildings’ operational diversity in a computational framework for high-resolution urban energy modeling. Build Simul, 2017, 10(6): 1005–1021.