Energy and AI (Apr 2023)
SolarGAN: Synthetic annual solar irradiance time series on urban building facades via Deep Generative Networks
Abstract
Building Integrated Photovoltaics (BIPV) is a promising technology to decarbonize urban energy systems via harnessing solar energy available on building envelopes. While methods to assess solar irradiation, especially on rooftops, are well established, the assessment on building facades usually involves a higher effort due to more complex urban features and obstructions. The drawback of existing physics-based simulation programs are that they require significant manual modeling effort and computing time for generating time resolved deterministic results. Yet, solar irradiation is highly intermittent and representing its inherent uncertainty may be required for designing robust BIPV energy systems. Targeting on these drawbacks, this paper proposes a data-driven model based on Deep Generative Networks (DGN) to efficiently generate stochastic ensembles of annual hourly solar irradiance time series on building facades with uncompromised spatiotemporal resolution at the urban scale. The only input required are easily obtainable fisheye images as categorical shading masks captured 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 time-consuming simulations, when facing lacking information (e.g., no 3D model exists), and to use the generated stochastic time-series ensembles in robust energy systems planning. Our validations exemplify a good fidelity of the generated time series when compared to the physics-based simulator. Due to the nature of the used DGNs, it remains an open challenge to precisely reconstruct the ground truth one-to-one for each hour of the year. However, we consider the benefits of the approach to outweigh the shortcomings. To demonstrate the model’s relevance for urban energy planning, we showcase its potential for generative design by parametrically altering characteristic features of the urban environment and producing corresponding time series on building facades under different climatic contexts in real-time.
