Revolutionizing Renewable Energy Through Artificial Intelligence: An Overview
Lecture Notes in Information Systems and Organisation, ISSN: 2195-4976, Vol: 71 LNISO, Page: 56-65
2024
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Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Conference Paper Description
Recently, the domains of artificial intelligence (AI) and renewable energy (RE) are increasingly overlapping. AI technologies are being employed more and more to support the development, implementation, and administration of sustainable energy resources due to their capacity to handle complex and nonlinear data structures. The generation, delivery, and consumption of green energy may become more affordable, dependable, and efficient as a result of this merger. To have a deep understanding and a comprehensive overview of the potential presented by these two emerging fields, this study offers an overview to the core principles of AI and renewable energies and explores the collaborative benefits resulting from the union of these technologies.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85211920155&origin=inward; http://dx.doi.org/10.1007/978-3-031-75329-9_7; https://link.springer.com/10.1007/978-3-031-75329-9_7; https://dx.doi.org/10.1007/978-3-031-75329-9_7; https://link.springer.com/chapter/10.1007/978-3-031-75329-9_7
Springer Science and Business Media LLC
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