Compared to conventional photovoltaic (PV), there are more influencing factors in bifacial photovoltaics to be considered to calculate incoming irradiance and energy yield. Accurate models to investigate the influences of the elevation, the albedo of the ground, the shading conditions between the PV rows and many other rear-side related factors are required. This paper combines the ray tracing (RT) and view factor (VF) models to calculate the irradiance with a subsequent electrical yield calculation using the one-diode model. To verify the results of the developed open-source simulation program BifacialSimu, accurate data from a plant in Golden, USA (single-axis tracked) and a commercially operated plant in Germany (fixed-tilt) are used. Through comparisons to the actual data, it can be concluded that a combination of RT and VF models seems to be valid for longer simulation periods with several months since the relative errors balance out. The RT-only simulation accurately reproduces the precise hourly radiation and electrical yield pattern. Still, a continuous positive deviation was found, which does not even out over long periods and is thus less accurate than the VF/RT combination. A simulation for a single month with RT can take several hours. Thus, the best simulation mode results according to user requirements.

The annual yield of bifacial photovoltaic systems is highly dependent on the albedo of the underlying soil. There are currently no published data about the albedo of red soil in western Africa. In this study, the impact of the albedo of red soil in Ghana on the energy yield of bifacial photovoltaic systems is analysed. A bifacial photovoltaic simulation model is created by combining the optical view factor matrix with an electrical output simulation. For an exact simulation, the albedo of red soil at three different locations in Ghana is measured for the first time. The average albedo of every red soil is clearly determined, as well as the measurement span including instrumentation uncertainty; values between 0.175 and 0.335 were measured. Considering these data, a state-of-the-art bifacial photovoltaic system with an average of 19.8% efficient modules in northern Ghana can achieve an annual energy yield of 508.8 kWh/m² and a bifacial gain of up to 18.3% in comparison with monofacial photovoltaic panels. To summarise, red soil in two out of three locations in Ghana shows higher albedo values than most natural ground surfaces and therefore positively impacts the annual yield of bifacial photovoltaic systems.

Augmented reality (AR) offers promising opportunities to support manual assembly, but there is little consensus on how much information AR instructions should contain, reflecting debates between cognitive-load-oriented minimalism and multimedia-learning-based benefits of redundancy. These debates manifest in practice as rich, multimodal overlays or minimal, complexity-adaptive visualizations designed to avoid clutter and ease authoring. This study compares these approaches by contrasting a redundant AR concept combining three-dimensional models, photographs, and videos with a minimal concept that adapts visualization types to assembly step complexity. In a between-subject experiment with 30 participants (mixed-experience; heterogeneous backgrounds) performing a heat-pump assembly task for the first time in a spatially constrained setup, errors, task time, workload, and usability were measured. The redundant concept led to significantly fewer errors and a lower per-step error probability than the minimal concept, without a penalty in assembly time. Workload and usability were comparable across concepts and primarily driven by performance rather than by visualization style. Step complexity strongly predicted completion time but not error rates, suggesting that operators slow down on complex steps while failures are more sensitive to instructional design. These findings suggest that overly minimal AR instructions increase error risk, whereas redundant AR instructions stabilize performance.

The multimodal main format of the TikTok channel @energiewende.erklaert, in which the communicating researcher answers questions about the energy transition, requires her to read and approve comments under her own videos. Verbal attacks in the form of polarising comments are not only a burden, but also influence the performance of the communicating researcher and her way of doing science communication. These attacks will be analysed and evaluated with the help of a qualitative data analysis of the comments. In order to analyse the comments comprehensively, to create comparable conditions and to give the researcher self-efficacy, a methodology is developed in which the participation and separation of the communicating researcher is weighted and balanced differently depending on the process step. It is shown that the participation of the communicating researcher in the research process offers advantages, for example in the case of access restrictions by the platform and through her specialist expertise. At the same time, the biases and distortions caused by self-research must and can be minimised through transparency and a clear division of responsibilities, especially in qualitative analysis and evaluation.