Search

This study evaluates and compares two types of interspersed bifacial agrivoltaic systems in Belgium: a fixed vertical system and a dynamic single-axis tracker, focusing on sugarbeet cultivation. Additionally, the impact of different tracking algorithms on crop yield and quality is investigated. The main approach involves developing an empirical crop model based on radiation use efficiency (RUE) and utilizing a radiation tool to simulate crop yield and quality. Field measurements of electricity and crop output are conducted over a span of two years, 2021 and 2022. The findings reveal that the dynamic solar tracker outperforms the fixed vertical bifacial setup in both years. The smart-tracking algorithm, applied under optimal watering conditions in the 2021 season, leads to a significant increase in energy yield (＋30%) and land use efficiency (＋20%) at a lower cost, while maintaining comparable crop yields to the vertical setup. However, limitations are observed in the empirical crop growth model's ability to account for climatic variability in the dry 2022 year, reducing its usefulness during the design phase. Notably, the practical implementation of the systems highlights challenges during construction and exploitation, emphasizing the significance of considering these practical factors together with climate, structure and crop choice assessing the effectiveness and to de-risk investments. [Display omitted] • A comparative study: bifacial agrivoltaic vertical vs. single-axis tracker system. • Dynamic single-axis solar tracker outperforms vertical bifacial setup. • Highest increase in land efficiency (＋47%) measured in hot, dry conditions. • No land efficiency increase observed in optimal watering conditions. • Construction and operational challenges significantly impact results. [ABSTRACT FROM AUTHOR]

Spatial and temporal variability of rainfall input plays a critical role on the performance of the urban runoff simulations. For this reason, urban hydrological application demands accurate products of high-resolution and high-quality rainfall estimates, which use and understanding become critical for more robust models. In this study, the spatial and temporal characteristics of rainfall in Belgium are analyzed at fine scales. This is done by constructing a conceptual rain storm model using historical long-term radar data archive. This application involved the use of a state-of-the-art tracking algorithm, developed by Muñoz et al. (2018), to associate the spatial and temporal variations in rain storms and cells This algorithm enables better handling of high-resolution details and is capable of extracting rainfall objects/entities across various scales. Through tracking and conceptualizing these storm entities, spatial and temporal rainfall properties, such as geometric features, intensities, velocity, rainfall types or spatial patterns, can be extracted and analyzed. To accomplish this, twelve years of continuous high-resolution radar data are utilized in this work. The resulting analysis of the spatial and temporal characteristics of rainfall radar data is intended to set the ground for the future applications. More specifically, it can be used to refine and improve the existing long-term spatial rainfall generators, such as that the one implemented by Willems' in 2001. • Spatial and temporal rainfall features over Belgium are characterized. • A total of 12 years of 5-min high-resolution (C-band) radar data is analyzed. • A state-of-the-art cell tracking algorithm is employed to identify individual rain cells from radar images. • A multi-layer conceptual rainstorm model is proposed to enable analyzing storm entities across various scales. • Significant correlations between various rainfall features are observed. [ABSTRACT FROM AUTHOR]

This paper presents a technique and experimental validation for anonymous outdoor location tracking of all users residing on a mobile cellular network. The proposed technique does not require any intervention or cooperation on the mobile side but runs completely on the network side, which is useful to automatically monitor traffic, estimate population movements, or detect criminal activity. The proposed technique exploits the topology of a mobile cellular network, enriched open map data, mode of transportation, and advanced route filtering. Current tracking algorithms for cellular networks are validated in optimal or controlled environments on a small dataset or are merely validated by simulations. In this work, validation data consisting of millions of parallel location estimations from over a million users are collected and processed in real time, in cooperation with a major network operator in Belgium. Experiments are conducted in urban and rural environments near Ghent and Antwerp, with trajectories on foot, by bike, and by car, in the months May and September 2017. It is shown that the mode of transportation, smartphone usage, and environment impact the accuracy and that the proposed AMT location tracking algorithm is more robust and outperforms existing techniques with relative improvements up to 88%. Best performances were obtained in urban environments with median accuracies up to 112 m. [ABSTRACT FROM AUTHOR]