Analysis of Ocean Wind Energy Density around Sulawesi and Maluku Islands with Scatterometer Data

Mahmuddin, Faisal; Idrus, Misliah; Hamzah,

doi:10.1016/j.egypro.2015.01.041

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Similar concept has also been introduced by Mahmuddin et al [4,5] to harvest ocean wind energy around Sulawesi and Maluku islands sea areas. The structure is known to be mobile floating structure (MFS).…”

Section: Introductionmentioning

confidence: 87%

Comparison of Remotely Sensed Wind Data over Sulawesi and Maluku Islands Sea Areas

Mahmuddin¹

2016

KEG

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<p class="TTPKeywords"><span>In order to obtain accurate prediction of ocean wind energy, long term data are needed. However, one data sources might not able to provide long duration data. Therefore, the data need to be combined with other sources of data. However, before combining the data, it is important to compare and validate them to confirm their accuracy. In the present study, wind speed data collected by QuikScat and SSM/I (SSMIS) missions are compared and analyzed. QuikScat data were collected by a satellite with the same name, while Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data are processed and offered by Remote Sensing System (RSS). SSM/I (SSMIS) are passive microwave radiometers carried onboard Defense Meteorological Satellite Program (DMSP). For the comparison, 5 (five) arbitrary positions over Sulawesi and Maluku islands sea areas are chosen for the analyses. For the evaluation purposes, beside time series of daily data from several chosen positions in research location, several statistical parameters are also computed and compared such as mean, standard deviation, root mean square (RMS), correlation coefficient. </span></p>

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Section: Introductionmentioning

confidence: 87%

Comparison of Remotely Sensed Wind Data over Sulawesi and Maluku Islands Sea Areas

Mahmuddin¹

2016

KEG

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“…Several methods are used to analyze wind energy potential, including observationbased analysis [4], satellite remote sensing analysis [5][6][7], model-based analysis [8,9], and reanalysis data-based analysis [10,11]. Recently, the study of wind energy potential with artificial neural network-based models has received attention [12].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Wind Energy Potential in Sri Lankan Waters Based on ERA5 (ECMWF Reanalysis v5) and CCMP (Cross-Calibrated Multi-Platform)

Yao,

Miao,

Kumara

et al. 2024

JMSE

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Amid global energy demands and environmental concerns, the exploration of renewable energy sources has become critically important. This study presents a comprehensive analysis of wind energy dynamics over a 35-year period (1988–2022) using CCMP and ERA5 datasets. It focuses on the spatial and temporal characteristics of wind energy within the region, particularly its distribution. The wind field in Sri Lanka has significant seasonal and regional characteristics. Despite seasonal variations, the overall wind activity remains moderate, with speeds generally above 5.4 m/s and annual average wind power densities exceeding 150 W/m2, reaching up to 200 W/m2 in certain areas. The two primary wind directions in the waters near Sri Lanka are WSW and NE. The study identifies periods of high stability, particularly from June to August, where effective wind speed occurrence (EWSO) exceeds 80%. A ‘W’-shaped pattern in monthly variations corresponds with changes in wind power density (WPD), highlighting optimal periods for wind energy extraction. Additionally, estimated reserves and technically exploitable wind energy resources (WERs) suggest that wind energy development off Sri Lanka is feasible, with potential capacities of approximately 2.00 GW and 1.60 GW, respectively. The overall coefficient of variation (CV) is small, indicating stable wind energy conditions. This analysis not only provides a scientific basis for evaluating WERs near Sri Lanka but also offers valuable insights for strategic planning and development in the renewable energy sector.

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“…Distributions and temporal variations of surface winds in specific regions have been investigated using satellite scatterometers or satellite‐based wind data. The QuikSCAT wind data are used to examine the winds in the Java Sea (Hattori et al ., 2011), the Makassar Strait (Gordon et al ., 2003; Susanto et al ., 2012), the seas around Sulawesi and Molucca Islands (Mahmuddin and Hamzah, 2015), and the Halmahera Sea (Setiawan et al ., 2019). Wirasatriya et al .…”

Section: Introductionmentioning

confidence: 99%

Seasonal distribution and variability of surface winds in the Indonesian seas using scatterometer and reanalysis data

Alifdini

Shimada

Wirasatriya

2021

Intl Journal of Climatology

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The distribution and variability of surface winds in the seas and straits in the Maritime Continent, or the Indonesian seas, are investigated using scatterometer wind measurements and reanalysis wind data. This study focuses on the detailed climatology of surface winds in the Indonesian seas and has significant implications for a better understanding of regional climate, meteorological disasters, and offshore wind resource. The monsoon winds predominantly blow over the seas along the routes formed by the coastlines of the large islands and the chains of the small islands. The wind speeds are persistently strong along these routes, and the major direction of the variability in winds is well aligned with these routes. The wind jets are formed in the central part of the routes. The interisland gaps and channels form wind jets of various scales. The Australian winter monsoon is stronger and more persistent than the East Asian winter monsoon in the Indonesian seas. The onset of each monsoon differs by approximately 40 days between the east and west of the Indonesian seas. The amplitudes and spatial extents of diurnally varying winds dominate along the northern coasts of Java Island and the small islands to the east, along the northwest and northeast coasts of Borneo, along the coast of southern Sulawesi Island, and to the southwest of Papua, and vanish along the centre lines of the monsoon routes. Sea areas with a large amplitude of diurnally varying wind are much the same throughout the year. The amplitude of diurnally varying wind has an annual maximum in September in the southern Indonesian seas, possibly in correspondence with the annual maximum of the temperature difference between land and sea.

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Analysis of Ocean Wind Energy Density around Sulawesi and Maluku Islands with Scatterometer Data

Cited by 6 publications

References 4 publications

Comparison of Remotely Sensed Wind Data over Sulawesi and Maluku Islands Sea Areas

Comparison of Remotely Sensed Wind Data over Sulawesi and Maluku Islands Sea Areas

Analysis of Wind Energy Potential in Sri Lankan Waters Based on ERA5 (ECMWF Reanalysis v5) and CCMP (Cross-Calibrated Multi-Platform)

Seasonal distribution and variability of surface winds in the Indonesian seas using scatterometer and reanalysis data

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