Multi-objective algorithm for the design of prediction intervals for wind power forecasting model
• A new algorithm based on multi-objective formulation is applied to design the prediction intervals for wind power. • Data pre-process strategy based on feature extraction is built to reduce the complexity and determine the input forms. • The wind speed prediction intervals are estimated through ma...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jiang, Ping [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Umfang: |
22 |
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| Übergeordnetes Werk: |
Enthalten in: Early Cretaceous mafic dykes from the Chhota Nagpur Gneissic Terrane, eastern India: Evidence of multiple magma pulses for the main stage of the Greater Kerguelen mantle plume - Srivastava, Rajesh K. ELSEVIER, 2022, simulation and computation for engineering and environmental systems, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:67 ; year:2019 ; pages:101-122 ; extent:22 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.apm.2018.10.019 |
|---|
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ELV045280193 |
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| 245 | 1 | 0 | |a Multi-objective algorithm for the design of prediction intervals for wind power forecasting model |
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| 520 | |a • A new algorithm based on multi-objective formulation is applied to design the prediction intervals for wind power. • Data pre-process strategy based on feature extraction is built to reduce the complexity and determine the input forms. • The wind speed prediction intervals are estimated through machine learning method. • Fuzzy set theory selection method is applied to extract the best compromise solution. | ||
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| 700 | 1 | |a Li, Hongmin |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Srivastava, Rajesh K. ELSEVIER |t Early Cretaceous mafic dykes from the Chhota Nagpur Gneissic Terrane, eastern India: Evidence of multiple magma pulses for the main stage of the Greater Kerguelen mantle plume |d 2022 |d simulation and computation for engineering and environmental systems |g Amsterdam [u.a.] |w (DE-627)ELV008859868 |
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10.1016/j.apm.2018.10.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001353.pica (DE-627)ELV045280193 (ELSEVIER)S0307-904X(18)30511-0 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Jiang, Ping verfasserin aut Multi-objective algorithm for the design of prediction intervals for wind power forecasting model 2019 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A new algorithm based on multi-objective formulation is applied to design the prediction intervals for wind power. • Data pre-process strategy based on feature extraction is built to reduce the complexity and determine the input forms. • The wind speed prediction intervals are estimated through machine learning method. • Fuzzy set theory selection method is applied to extract the best compromise solution. Li, Ranran oth Li, Hongmin oth Enthalten in Elsevier Science Srivastava, Rajesh K. ELSEVIER Early Cretaceous mafic dykes from the Chhota Nagpur Gneissic Terrane, eastern India: Evidence of multiple magma pulses for the main stage of the Greater Kerguelen mantle plume 2022 simulation and computation for engineering and environmental systems Amsterdam [u.a.] (DE-627)ELV008859868 volume:67 year:2019 pages:101-122 extent:22 https://doi.org/10.1016/j.apm.2018.10.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 67 2019 101-122 22 |
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Enthalten in Early Cretaceous mafic dykes from the Chhota Nagpur Gneissic Terrane, eastern India: Evidence of multiple magma pulses for the main stage of the Greater Kerguelen mantle plume Amsterdam [u.a.] volume:67 year:2019 pages:101-122 extent:22 |
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multi-objective algorithm for the design of prediction intervals for wind power forecasting model |
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Multi-objective algorithm for the design of prediction intervals for wind power forecasting model |
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• A new algorithm based on multi-objective formulation is applied to design the prediction intervals for wind power. • Data pre-process strategy based on feature extraction is built to reduce the complexity and determine the input forms. • The wind speed prediction intervals are estimated through machine learning method. • Fuzzy set theory selection method is applied to extract the best compromise solution. |
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• A new algorithm based on multi-objective formulation is applied to design the prediction intervals for wind power. • Data pre-process strategy based on feature extraction is built to reduce the complexity and determine the input forms. • The wind speed prediction intervals are estimated through machine learning method. • Fuzzy set theory selection method is applied to extract the best compromise solution. |
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• A new algorithm based on multi-objective formulation is applied to design the prediction intervals for wind power. • Data pre-process strategy based on feature extraction is built to reduce the complexity and determine the input forms. • The wind speed prediction intervals are estimated through machine learning method. • Fuzzy set theory selection method is applied to extract the best compromise solution. |
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Multi-objective algorithm for the design of prediction intervals for wind power forecasting model |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV045280193</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624114356.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">190205s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.apm.2018.10.019</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001353.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV045280193</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0307-904X(18)30511-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">38.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jiang, Ping</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-objective algorithm for the design of prediction intervals for wind power forecasting model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">22</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• A new algorithm based on multi-objective formulation is applied to design the prediction intervals for wind power. • Data pre-process strategy based on feature extraction is built to reduce the complexity and determine the input forms. • The wind speed prediction intervals are estimated through machine learning method. • Fuzzy set theory selection method is applied to extract the best compromise solution.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Ranran</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Hongmin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Srivastava, Rajesh K. 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