摘要
Building Integrated Photovoltaics (BIPV) is a promising technology to decarbonize urban energy systems viaharnessing solar energy available on building envelopes. While methods to assess solar irradiation, especiallyon rooftops, are well established, the assessment on building facades usually involves a higher effort due tomore complex urban features and obstructions. The drawback of existing physics-based simulation programsare that they require significant manual modeling effort and computing time for generating time resolveddeterministic results. Yet, solar irradiation is highly intermittent and representing its inherent uncertainty maybe required for designing robust BIPV energy systems. Targeting on these drawbacks, this paper proposes adata-driven model based on Deep Generative Networks (DGN) to efficiently generate stochastic ensembles ofannual hourly solar irradiance time series on building facades with uncompromised spatiotemporal resolutionat the urban scale. The only input required are easily obtainable fisheye images as categorical shading maskscaptured from 3D models. In principle, even actual photographs of urban contexts can be utilized, given they are semantically segmented. The potential of our approach is that it may be applied as a surrogate for timeconsuming simulations, when facing lacking information (e.g., no 3D model exists), and to use the generatedstochastic time-series ensembles in robust energy systems planning. Our validations exemplify a good fidelityof the generated time series when compared to the physics-based simulator. Due to the nature of the usedDGNs, it remains an open challenge to precisely reconstruct the ground truth one-to-one for each hour of theyear. However, we consider the benefits of the approach to outweigh the shortcomings. To demonstrate themodel’s relevance for urban energy planning, we showcase its potential for generative design by parametricallyaltering characteristic features of the urban environment and producing corresponding time series on buildingfacades under different climatic contexts in real-time.
