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Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning energies Article Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning Sebastian Krapf *, Nils Kemmerzell, Syed Khawaja Haseeb Uddin, Manuel Hack Vázquez, Fabian Netzler and Markus Lienkamp Department of Mechanical Engineering, Institute of Automotive Technology, School of Engineering & Design, Technical University of Munich, 85748 Garching, Germany; [email protected] (N.K.); [email protected] (S.K.H.U.); [email protected] (M.H.V.); [email protected] (F.N.); [email protected] (M.L.) * Correspondence: [email protected] Abstract: Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Citation: Krapf, S.; Kemmerzell, N.; Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof Khawaja Haseeb Uddin, S.; Hack segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a Vázquez, M.; Netzler, F.; Lienkamp, 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only M. Towards Scalable Economic publicly available data and is potentially scalable to the global level. However, this poses a variety Photovoltaic Potential Analysis Using of research challenges and opportunities, which are summarized with a focus on the application of Aerial Images and Deep Learning. deep learning, economic photovoltaic potential analysis, and energy system analysis. Energies 2021, 14, 3800. https:// doi.org/10.3390/en14133800 Keywords: photovoltaic economic potential; aerial images; deep learning; semantic segmentation; roof segments; roof superstructures; public data Academic Editor: Eun-Chel Cho Received: 20 May 2021 Accepted: 21 June 2021 Published: 24 June 2021 1. Introduction The world is increasing efforts to move towards renewable energy production. So- Publisher’s Note: MDPI stays neutral lar power is one important pillar in this endeavor for the massive reduction in carbon with regard to jurisdictional claims in dioxide emissions. Large-scale, free-standing photovoltaic (PV) plants achieve a low lev- published maps and institutional affil- elized cost of energy (LCOE) and can outperform fossil power plants in this matter [1–3]. iations. Though more expensive, rooftop PV systems play a major role in a sustainable energy system because they do not seal additional land area. In the past, the introduction of PV was enabled and accelerated by subsidies. To support policymakers, researchers have con- ducted rooftop PV potential analyses ranging from city-scale to country-scale. This paper Copyright: © 2021 by the authors. extends existing approaches by presenting a method for a scalable, individual building Licensee MDPI, Basel, Switzerland. economic PV potential estimation using publicly available data sources and deep learning This article is an open access article approaches. We first present the results and discuss experiences with the implemented distributed under the terms and methodology to outline challenges and research opportunities in this interdisciplinary field. conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Energies 2021, 14, 3800. https://doi.org/10.3390/en14133800 https://www.mdpi.com/journal/energies EnergiesEnergies2021 2021, 14, 14, 3800, x FOR PEER REVIEW 2 of2 of22 22 1.1.1.1. Existing PV Potential Analysis Analysis with with Respect Respect to to Potential Potential Type Type PVPV potentialpotential cancan bebe divideddivided intointo physical, physical, geographic, geographic, technical, technical, and and economic economic poten- po- tial,tential, whereas whereas the the level level of informationof information increases increases for for each each type type [4– [4–8].8]. Figure Figure1 visualizes 1 visualizes the PVthe potentialPV potential qualitatively. qualitatively. Physical Geographic Technical Economic Potential Potential Potential Potential FigureFigure 1.1. Visualization of the four PV potential types. types. TheThe physical potential is the solar radiation radiation at at a a geographic geographic location location and and consists consists of of direct,direct, diffuse,diffuse, and reflectedreflected radiation of of the the real-sky real-sky global global irradiance. irradiance. PV-GIS PV-GIS [9,10] [9,10 ]or or thethe CopernicusCopernicus Atmosphere Monitoring Service Service data data [11] [11] provide provide estimations estimations of of Europe’s Europe’s physicalphysical potentialpotential via Application Programming Programming Interfaces Interfaces ( (APIs).APIs). Some Some researchers researchers focus focus onon geographicgeographic PV potential, which is is the the available available solar solar radiation radiation on on roofs roofs considering considering the the roofroof planes’planes’ orientation and shadowing shadowing effects effects of of surrounding surrounding structures structures [12,13]. [12,13]. Publica- Publica- tionstions determinedetermine the technical potential, potential, which which takes takes the the PV PV system system efficiency efficiency into into account account andand representsrepresents the actual energy generation [5,8,14–21]. [5,8,14–21]. TheThe fourthfourth type of potential is the economic potential. Subsidies Subsidies and and policies policies have have majormajor leverageleverage on the economic potential. Fu Furthermore,rthermore, aa positive positive business business case case of of a aPV PV systemsystem is one of the most most important important drivers drivers fo forr installing installing PV PV systems. systems. Therefore, Therefore, for for poli- pol- icymakers,cymakers, it it is is important important to to know know the the technical technical potential, potential, but but it it is is even even more more critical critical to to understandunderstand thethe economiceconomic potential.potential. Hence, Hence, analyses analyses should should be be extended extended from from technical technical po- tentialpotential to economicto economic potential. potential. Recent Recent studies studies include include the the economic economic potential potential to to incorporate incorpo- onlyrate only the share the share of the of PV the potential, PV potential, which which is economically is economically viable viable[4,7, 22[4,7,22–27].–27]. The The economic eco- potentialnomic potential can be assessedcan be assessed by the LCOE,by the LCOE, a common a common benchmarking benchmarking measure measure for energy for en- gen- eration,ergy generation, which represents which represents the cost perthe generatedcost per generated kWh over kWh the over lifetime the lifetime of PV systems of PV sys- [28]. Atems comparison [28]. A comparison of the generation of the generation costs (e.g., costs LCOE) (e.g., with LCOE) an income with an threshold income suchthreshold as the feed-in-tariffssuch as the feed-in-tariffs can determine can the economicdetermine viabilitythe economic of a PV viability installation of [a4 ,PV7,22 ,installation23]. In addi- tion,[4,7,22,23]. publications In addition, consider publications revenue andconsider calculate revenue the return and calculate on investment the return [25 ,on26], invest- the net presentment [25,26], value the [24 ],net and present the payback value [24], period and [the24– 27payback]. This period approach [24–27]. provides This aapproach more de- tailedprovides profitability a more detailed estimation. profitability It also enables estimati comparingon. It also economicenables comparing potential economic internationally po- becausetential internationally it can include differentbecause it feed-in-tariffs can include different or additional feed-in-tariffs revenue considerations,or additional revenue such as self-consumptionconsiderations, such models. as self-consumption In Germany, withmodels. decreasing In Germany, feed-in-tariffs, with decreasing PV systems feed-in- are increasinglytariffs, PV systems designed are increasingly for self-consumption designed for purposes self-consumption and are coupled purposes with and stationary are cou- batterypled with storages stationary [29]. battery While itstorages is common [29]. for Wh studiesile it is oncommon site-level for tostudies consider on site-level a mix of self-to consumptionconsider a mix and of self-consumption grid feed-in [29–31 and], few grid studies, feed-in such[29–31], as the few ones studies, from such Lee etas al.the [ 25ones,26 ], considerfrom Lee itet on al. system [25,26],level. consider it on system level. 1.2.1.2. Existing PV Potential Anal Analysisysis with with Respect Respect to to Method Method BesidesBesides categorizing
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