Machine learning applications for urban photovoltaic potential estimation: A survey

Valderrama, Alvaro; Valle, Carlos; Allende, Héctor; Ibarra, Marcelo; Vásquez, Camila

doi:10.1016/j.neucom.2023.01.006

Cited by 3 publications

(1 citation statement)

References 108 publications

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“…ML approaches have aroused high interest in recent years in the field of the integration of renewable energies in urban environments. A recent state of the art collected the most relevant works employing these approaches and identified potential gaps and future lines of research, which are the following [128]:…”

Section: Detailed Analysis Of the Modeling Techniques Of Pvsc In Urba...mentioning

confidence: 99%

Modeling and Optimization of Photovoltaic Installations at Urban Scale

Fuster Palop

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The building sector in developed countries consumes 20% to 40% of the global primary energy, contributing to 30% of the CO 2 emissions. The population growth, particularly in urban areas, is expected to exacerbate this trend, compromising the sustainability goals outlined in international agreements and accelerating climate change. However, the increasing interest and political support in mature renewable energy sources, particularly solar photovoltaic (PV), offers improvement opportunities. The deployment of PV systems in urban areas presents advantages regarding emissions, economic, environmental, and social benefits, enhancing grid efficiency, increasing energy independence, and promoting sustainability awareness and community involvement. To increase the adoption of rooftop PV self-consumption (PVSC) systems in urban environments, studies on the PV potential can help overcome social barriers and enable public administration, utility companies, and private corporations to optimize energy planning, promote PVSC, and attract private investment in clean energy. Although most of the PV potential studies in the literature rely on geospatial physical models, the opportunities provided by Machine Learning (ML) and agile statistical approaches to reduce the high computational cost of the previous models are not fully exploited. These opportunities, especially in the economic potential assessment, remain scarce.

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Section: Detailed Analysis Of the Modeling Techniques Of Pvsc In Urba...mentioning

confidence: 99%

Modeling and Optimization of Photovoltaic Installations at Urban Scale

Fuster Palop

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A district-scale spatial distribution evaluation method of rooftop solar energy potential based on deep learning

Li,

Wang,

et al. 2024

Solar Energy

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Spatiotemporal Estimation of the Potential Adoption of Photovoltaic Systems on Urban Residential Roofs

Mejia,

Macedo,

Pinto

et al. 2024

Electronics

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The adoption of residential photovoltaic (PV) systems to mitigate the effects of climate change has been incentivized in recent years by government policies. Due to the impacts of these systems on the energy mix and the electrical grid, it is essential to understand how these technologies will expand in urban areas. To fulfill that need, this article presents an innovative method for modeling the diffusion of residential PV systems in urban environments that employs spatial analysis and urban characteristics to identify residences at the subarea level with the potential for installing PV systems, along with temporal analysis to project the adoption growth of these systems over time. This approach integrates urban characteristics such as population density, socioeconomic data, public environmental awareness, rooftop space availability, and population interest in new technologies. Results for the diffusion of PV systems in a Brazilian city are compared with real adoption data. The results are presented in thematic maps showing the spatiotemporal distribution of potential adopters of PV systems. This information is essential for creating efficient decarbonization plans because, while many households can afford these systems, interest in new technologies and knowledge of the benefits of clean energy are also necessary for their adoption.

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Machine learning applications for urban photovoltaic potential estimation: A survey

Cited by 3 publications

References 108 publications

Modeling and Optimization of Photovoltaic Installations at Urban Scale

Modeling and Optimization of Photovoltaic Installations at Urban Scale

A district-scale spatial distribution evaluation method of rooftop solar energy potential based on deep learning

Spatiotemporal Estimation of the Potential Adoption of Photovoltaic Systems on Urban Residential Roofs

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