Stochastic Solar Irradiance from Deep Generative Networks and their Application in BIPV Design

Abstract

Building Integrated Photovoltaics (BIPV) is a promising technology to decarbonize urban energy systems via harnessing solar energy available on building envelopes. Nevertheless, handling the trade-off between effort, speed and spatial-temporal resolution for 3D BIPV solar potential evaluation in a complex urban context has always been a challenging task. Existing physics-based solar simulation engines require significant manual modelling effort and computing time to obtain high-resolution deterministic results. Yet, solar irradiation is highly intermittent and representing its inherent uncertainty may be required for designing robust energy systems. Targeting these drawbacks, this paper proposes a data-driven model based on Deep Generative Networks (DGN) to efficiently generate high-fidelity stochastic ensembles of annual hourly urban solar irradiance time-series data with uncompromised spatial-temporal resolution at the urban scale. It requires only easily accessible data inputs, i.e., simple fisheye images as categorical masks, such as captured from Level of Details (LOD) 1 urban geometry models. Our validations exemplify the high fidelity of the generated solar time series when compared to the physics-based simulator. To demonstrate the model’s relevance for urban energy design, we apply it to the resilient design of a district multi-energy system (MES) with several hundreds of BIPV surfaces. Furthermore, we showcase the models’ potential for generative design by parametrically altering the urban environment and producing corresponding irradiation time-series in real-time.

3D Solar Irradiance Model for Non-Uniform Shading Environments Using Shading (Aperture) Matrix Enhanced by Local Coordinate System

“…To consider the impact of shading on adjacent buildings, there have been attempts to develop and integrate geographical approaches to calculate the solar irradiance onto the building walls [44][45][46]. More recently, advanced computational methods using neural networks [47], machine learning [48], and artificial intelligence (AI) [49] have been developed. These advanced methods are mainly for calculating the average and peak irradiance and are used for annual or seasonal and maximum energy yields.…”