A Visual Analytics Framework for Renewable Energy Profiling and Resource Planning

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dc.contributor.authorPammi, Ramakrishna P.en_US
dc.contributor.authorAfzal, Shehzaden_US
dc.contributor.authorDasari, Hari Prasaden_US
dc.contributor.authorYousaf, Muhammaden_US
dc.contributor.authorGhani, Sohaiben_US
dc.contributor.authorVenkatraman, Murali Sankaren_US
dc.contributor.authorHoteit, Ibrahimen_US
dc.contributor.editorAngelini, Marcoen_US
dc.contributor.editorEl-Assady, Mennatallahen_US
dc.date.accessioned2023-06-10T06:09:15Z
dc.date.available2023-06-10T06:09:15Z
dc.date.issued2023
dc.description.abstractRenewable energy growth is one of the focus areas globally against the backdrop of the global energy crisis and climate change. Energy planners are looking into clean, safe, affordable, and reliable energy generation sources for a net zero future. Countries are setting energy targets and policies prioritizing renewable energy, shifting the dependence on fossil fuels. The selection of renewable energy sources depends on the suitability of the region under consideration and requires analyzing relevant environmental datasets. In this work, we present a visual analytics framework that facilitates users to explore solar and wind energy datasets consisting of Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), Diffusive Horizontal Irradiance (DHI), and Wind Power (WP) spanning across a 40 year period. This framework provides a suite of interactive decision support tools to analyze spatiotemporal patterns, variability in the variables across space and time at different temporal resolutions, Typical Meteorological Year (TMY) data with varying percentiles, and provides the capability to interactively explore and evaluate potential solar and wind energy equipment installation locations and study different energy acquisition scenarios. This work is conducted in collaboration with domain experts involved in sustainable energy planning. Different use case scenarios are also explained in detail, along with domain experts feedback and future directions.en_US
dc.description.sectionheadersDecision-making and Explanation
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.identifier.doi10.2312/eurova.20231096
dc.identifier.isbn978-3-03868-222-6
dc.identifier.issn2664-4487
dc.identifier.pages49-54
dc.identifier.pages6 pages
dc.identifier.urihttps://doi.org/10.2312/eurova.20231096
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20231096
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing -> Visual analytics; Applied computing -> Environmental sciences
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectApplied computing
dc.subjectEnvironmental sciences
dc.titleA Visual Analytics Framework for Renewable Energy Profiling and Resource Planningen_US
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