Assessing the Potential of Rooftop Photovoltaics by Processing High-Resolution Irradiation Data, as Applied to Giessen, Germany

Hannes Koch (), Stefan Lechner, Sebastian Erdmann and Martin Hofmann
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Hannes Koch: Institute of Thermodynamics, Energy Process Engineering and System Engineering (THESA), Technische Hochschule Mittelhessen—University of Applied Sciences, Wiesenstrasse 14, 35390 Giessen, Germany
Stefan Lechner: Institute of Thermodynamics, Energy Process Engineering and System Engineering (THESA), Technische Hochschule Mittelhessen—University of Applied Sciences, Wiesenstrasse 14, 35390 Giessen, Germany
Sebastian Erdmann: Institute of Thermodynamics, Energy Process Engineering and System Engineering (THESA), Technische Hochschule Mittelhessen—University of Applied Sciences, Wiesenstrasse 14, 35390 Giessen, Germany
Martin Hofmann: Institute for Electrical Engineering and Information Technology (EI), Technische Hochschule Mittelhessen—University of Applied Sciences, Wiesenstrasse 14, 35390 Giessen, Germany

Energies, 2022, vol. 15, issue 19, 1-17

Abstract: In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km 2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.

Keywords: rooftop solar energy potential; renewable resources assessment; rooftop surface analysis; data processing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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