A density‐based time‐series data analysis methodology for shadow detection in rooftop photovoltaic systems
Publication date
2023-05
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Abstract
The majority of photovoltaic (PV) systems in the Netherlands are small scale, installed on rooftops, where the lack of onsite global tilted irradiance (GTI) measurements and the frequent presence of shadow due to objects in the close vicinity oppose challenge in their monitoring process. In this study, a new algorithmic tool is introduced that creates a reference data-set through the combination of data-sets of the unshaded PV systems in the surrounding area. It subsequently compares the created reference data-set with the one of the PV system of interest, detects any energy loss and clusters the distinctive loss due to shadow, created by the surrounding objects. The new algorithm is applied successfully to a number of different cases of shaded PV systems. Finally, suggestions on the unsupervised use of the algorithm by any monitoring platform are discussed, along with its limitations algorithm and suggestions for further research.
Keywords
cluster analysis, density-based spatial clustering of applications with noise (DBSCAN), malfunction detection, monitoring, photovoltaic systems, shadow detection, Electronic, Optical and Magnetic Materials, Condensed Matter Physics, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment, SDG 7 - Affordable and Clean Energy
Citation
Tsafarakis, O & Sark, W G J H M V 2023, 'A density‐based time‐series data analysis methodology for shadow detection in rooftop photovoltaic systems', Progress in Photovoltaics: Research and Applications, vol. 31, no. 5, pp. 506-523. https://doi.org/10.1002/pip.3654