A new innovative methodology for photovoltaic integration on rooftops for cost reduction and reduced grid dependency

Edisson Villa-Ávila, Antonio Cano, Paul Arévalo, Danny Ochoa-Correa, Francisco Jurado

Producción científica: Capítulo del libro/informe/acta de congresoCapítulorevisión exhaustiva

Resumen

As the global demand for sustainable energy rises, accurately assessing the potential of solar energy becomes crucial. Photovoltaic systems, particularly those integrated into urban rooftops, offer a promising solution to address challenges associated with outdated energy grids and increasing fossil fuel costs. However, achieving the optimal placement of photovoltaic panels on rooftops remains challenging due to factors such as building morphology, location, and the surrounding environment. In this context, this chapter introduces the roof-solar methodology, designed to enhance the placement of photovoltaic panels in urban environments by avoiding shading and overlaps. Based on geographic information system technologies and three-dimensional models, this methodology provides precise estimates of photovoltaic energy generation potential. Key contributions of this work include a roof categorization model, the identification of suitable roofs for photovoltaic panel installation, as well as the optimal spatial distribution of these devices, and innovative assessment technologies. The practical application of this methodology in real urban environments confirms its utility for decision-making in the planning and development of solar energy systems. The results highlight a significant potential for photovoltaic energy generation in the studied urban area, with capacities reaching up to 343 kW. Furthermore, implementing photovoltaic systems on residential rooftops have proven to be an effective strategy in reducing CO2 emissions and addressing climate change, contributing to a cleaner and more sustainable energy matrix in urban settings.

Idioma originalInglés
Título de la publicación alojadaArtificial Intelligence and Machine Learning Applications for Sustainable Development
EditorialCRC Press
Páginas150-176
Número de páginas27
ISBN (versión digital)9781040273586
ISBN (versión impresa)9781040273586
DOI
EstadoPublicada - 1 ene. 2025

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NombreArtificial Intelligence and Machine Learning Applications for Sustainable Development

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