Wind ramp event prediction with parallelized Gradient Boosted Regression\n Trees Article Swipe
Saurav Gupta
,
Nitin Anand Shrivastava
,
Abbas Khosravi
,
Bijaya Ketan Panigrahi
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1610.05009
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1610.05009
Accurate prediction of wind ramp events is critical for ensuring the\nreliability and stability of the power systems with high penetration of wind\nenergy. This paper proposes a classification based approach for estimating the\nfuture class of wind ramp event based on certain thresholds. A parallelized\ngradient boosted regression tree based technique has been proposed to\naccurately classify the normal as well as rare extreme wind power ramp events.\nThe model has been validated using wind power data obtained from the National\nRenewable Energy Laboratory database. Performance comparison with several\nbenchmark techniques indicates the superiority of the proposed technique in\nterms of superior classification accuracy.\n
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Metadata
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/1610.05009
- https://arxiv.org/pdf/1610.05009
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4301914731
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4301914731Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.1610.05009Digital Object Identifier
- Title
-
Wind ramp event prediction with parallelized Gradient Boosted Regression\n TreesWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2016Year of publication
- Publication date
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2016-10-17Full publication date if available
- Authors
-
Saurav Gupta, Nitin Anand Shrivastava, Abbas Khosravi, Bijaya Ketan PanigrahiList of authors in order
- Landing page
-
https://arxiv.org/abs/1610.05009Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1610.05009Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1610.05009Direct OA link when available
- Concepts
-
Wind power, Computer science, Benchmark (surveying), Regression, Reliability (semiconductor), Event (particle physics), Wind speed, Renewable energy, Data mining, Power (physics), Meteorology, Statistics, Mathematics, Engineering, Geography, Electrical engineering, Quantum mechanics, Geodesy, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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