Wave energy resource classification system for US coastal waters
Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ahn, Seongho [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease - Soke, Fatih ELSEVIER, 2019, an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:104 ; year:2019 ; pages:54-68 ; extent:15 |
| Links: |
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| DOI / URN: |
10.1016/j.rser.2019.01.017 |
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| 520 | |a Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. | ||
| 520 | |a Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. | ||
| 650 | 7 | |a Wave resource classification |2 Elsevier | |
| 650 | 7 | |a Wave power |2 Elsevier | |
| 650 | 7 | |a Wave energy assessment |2 Elsevier | |
| 650 | 7 | |a Annual Available Energy |2 Elsevier | |
| 650 | 7 | |a WEC operating period bandwidth |2 Elsevier | |
| 650 | 7 | |a Peak period partition |2 Elsevier | |
| 700 | 1 | |a Haas, Kevin A. |4 oth | |
| 700 | 1 | |a Neary, Vincent S. |4 oth | |
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10.1016/j.rser.2019.01.017 doi GBV00000000000520.pica (DE-627)ELV045771014 (ELSEVIER)S1364-0321(19)30027-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl 44.65 bkl Ahn, Seongho verfasserin aut Wave energy resource classification system for US coastal waters 2019transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. Wave resource classification Elsevier Wave power Elsevier Wave energy assessment Elsevier Annual Available Energy Elsevier WEC operating period bandwidth Elsevier Peak period partition Elsevier Haas, Kevin A. oth Neary, Vincent S. oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:104 year:2019 pages:54-68 extent:15 https://doi.org/10.1016/j.rser.2019.01.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 104 2019 54-68 15 |
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10.1016/j.rser.2019.01.017 doi GBV00000000000520.pica (DE-627)ELV045771014 (ELSEVIER)S1364-0321(19)30027-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl 44.65 bkl Ahn, Seongho verfasserin aut Wave energy resource classification system for US coastal waters 2019transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. Wave resource classification Elsevier Wave power Elsevier Wave energy assessment Elsevier Annual Available Energy Elsevier WEC operating period bandwidth Elsevier Peak period partition Elsevier Haas, Kevin A. oth Neary, Vincent S. oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:104 year:2019 pages:54-68 extent:15 https://doi.org/10.1016/j.rser.2019.01.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 104 2019 54-68 15 |
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10.1016/j.rser.2019.01.017 doi GBV00000000000520.pica (DE-627)ELV045771014 (ELSEVIER)S1364-0321(19)30027-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl 44.65 bkl Ahn, Seongho verfasserin aut Wave energy resource classification system for US coastal waters 2019transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. Wave resource classification Elsevier Wave power Elsevier Wave energy assessment Elsevier Annual Available Energy Elsevier WEC operating period bandwidth Elsevier Peak period partition Elsevier Haas, Kevin A. oth Neary, Vincent S. oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:104 year:2019 pages:54-68 extent:15 https://doi.org/10.1016/j.rser.2019.01.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 104 2019 54-68 15 |
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10.1016/j.rser.2019.01.017 doi GBV00000000000520.pica (DE-627)ELV045771014 (ELSEVIER)S1364-0321(19)30027-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl 44.65 bkl Ahn, Seongho verfasserin aut Wave energy resource classification system for US coastal waters 2019transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. Wave resource classification Elsevier Wave power Elsevier Wave energy assessment Elsevier Annual Available Energy Elsevier WEC operating period bandwidth Elsevier Peak period partition Elsevier Haas, Kevin A. oth Neary, Vincent S. oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:104 year:2019 pages:54-68 extent:15 https://doi.org/10.1016/j.rser.2019.01.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 104 2019 54-68 15 |
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10.1016/j.rser.2019.01.017 doi GBV00000000000520.pica (DE-627)ELV045771014 (ELSEVIER)S1364-0321(19)30027-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl 44.65 bkl Ahn, Seongho verfasserin aut Wave energy resource classification system for US coastal waters 2019transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. Wave resource classification Elsevier Wave power Elsevier Wave energy assessment Elsevier Annual Available Energy Elsevier WEC operating period bandwidth Elsevier Peak period partition Elsevier Haas, Kevin A. oth Neary, Vincent S. oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:104 year:2019 pages:54-68 extent:15 https://doi.org/10.1016/j.rser.2019.01.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 104 2019 54-68 15 |
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Enthalten in Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease Amsterdam [u.a.] volume:104 year:2019 pages:54-68 extent:15 |
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Enthalten in Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease Amsterdam [u.a.] volume:104 year:2019 pages:54-68 extent:15 |
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Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease |
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They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. 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Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. |
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Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. |
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Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period ( T p , s ). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. |
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