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Building Integrated Photovoltaics (BIPV) is a promising technology todecarbonize urban energy systems via harnessing solar energy available onbuilding envelopes. While methods to assess solar irradiation, especially onrooftops, are well established, the assessment on building facades usuallyinvolves a higher effort due to more complex urban features and obstructions.The drawback of existing physics-based simulation programs is that they requiresignificant manual modelling effort and computing time for generating timeresolved deterministic results. Yet, solar irradiation is highly intermittentand representing its inherent uncertainty may be required for designing robustBIPV energy systems. Targeting on these drawbacks, this paper proposes adata-driven model based on Deep Generative Networks (DGN) to efficientlygenerate high-fidelity stochastic ensembles of annual hourly solar irradiancetime series on building facades with uncompromised spatiotemporal resolution atthe urban scale. The only input required is easily obtainable, simple fisheyeimages as categorical shading masks captured from 3D models. In principle, evenactual photographs of urban contexts can be utilized, given they aresemantically segmented. Our validations exemplify the high fidelity of thegenerated time series when compared to the physics-based simulator. Todemonstrate the model's relevance for urban energy planning, we showcase itspotential for generative design by parametrically altering characteristicfeatures of the urban environment and producing corresponding time series onbuilding facades under different climatic contexts in real-time.

SolarGAN: Synthetic Annual Solar Irradiance Time Series on Urban Building Facades via Deep Generative Networks