Use of Machine Learning Techniques on Aerial Imagery for the Extraction of Photovoltaic Data within the Urban Morphology

Fabio Giussani (), Eric Wilczynski, Claudio Zandonella Callegher, Giovanni Dalle Nogare, Cristian Pozza, Antonio Novelli and Simon Pezzutto
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Fabio Giussani: Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy
Eric Wilczynski: Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy
Claudio Zandonella Callegher: Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy
Giovanni Dalle Nogare: Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy
Cristian Pozza: Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy
Antonio Novelli: RHEA Group, Via di Grotte Portella 28, Edificio Clorofilla, Scala C, Piano 3, 00044 Frascati, Italy
Simon Pezzutto: Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy

Sustainability, 2024, vol. 16, issue 5, 1-16

Abstract: Locating and quantifying photovoltaic (PV) installations is a time-consuming and labor-intensive process, but it is necessary for monitoring their distribution. In the absence of existing data, the use of aerial imagery and automated detection algorithms can improve the efficiency and accuracy of the data collection process. This study presents a machine learning approach for the analysis of PV installations in urban areas based on less complex and resource-intensive models to target the challenge of data scarcity. The first objective of this work is to develop a model that can automatically detect PV installations from aerial imagery and test it based on the case study of Crevillent, Spain. Subsequently, the work estimates the PV capacity in Crevillent, and it compares the distribution of PV installations between residential and industrial areas. The analysis utilizes machine learning techniques and existing bottom-up data to assess land use and building typology for PV installations, identifying deployment patterns across the town. The proposed approach achieves an accuracy of 67% in detecting existing PV installations. These findings demonstrate that simple machine learning models still provide a reliable and cost-effective way to obtain data for decision-making in the fields of energy and urban planning, particularly in areas with limited access to existing data. Combining this technology with bottom-up data can lead to more comprehensive insights and better outcomes for urban areas seeking to optimize and decarbonize their energy supply while minimizing economic resources.

Keywords: photovoltaic; installed capacity; machine learning; object detection; aerial imagery; urban morphology; building stock analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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