A biomimetic approach for modeling cloud shading with dynamic behavior. (October 2016)

• Record Type: Journal Article
• Authors: García, Jesús M.; Padilla, Ricardo Vasquez; Sanjuan, Marco E.
• Is Part Of: Renewable energy. Volume 96:Part A(2016)
• Page Start: 157
• Page End: 166
• Publication Date: 2016-10
• Subjects: Bacteria colony -- Agent based modeling -- Dynamic shading -- Fuzzy; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2016.04.070
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Clouds are a complex phenomenon which is the result of a strong interaction between multiple variables. Modeling its behavior through physical principles is a task that requires time and is computationally demanding. One of the main effects caused by clouds are the shadows produced over the earth's surface, a phenomenon inherently complex due its origin. This paper proposes a computationally low-demanding model for imitating (not predicting) the behavior of cloud shading by applying a biomimetic approach. This analogy relays on using a bacterial colony growth behavior. The aim of this paper is to establish a methodology to develop a cloud-shading model useful for transient analysis in solar fields. The proposed model is evaluated qualitative and quantitative by comparing it with a model based on fractal surfaces and with real sky images. The qualitative evaluation indicates that shadows created by the proposed model change its shapes and move as seen in the real phenomenon. On the other hand, the quantitative assessment is accomplished through the Fast Fourier Transform analysis. This analysis indicates that the proposed model is able to achieve the performance shown by real cloud images. Graphical abstract: Highlights: Analogy using bacterial agents based algorithm to model cloud clusters behavior. Low computational load reached using matrix operations instead of loop structures. Flexible and easily-tunable model for matching real phenomenon images. Main features of agents are maintenance, reproduction and displacement.