Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets
Offshore wind farms are a current area of interest in Australia due to their ability to support its transition to renewable energy. Climate reanalysis datasets that provide simulated wind speed data are frequently used to evaluate the potential of proposed offshore wind farm locations. However, ther...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/8/3404 |
| _version_ | 1797605654497591296 |
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| author | Emily Cowin Changlong Wang Stuart D. C. Walsh |
| author_facet | Emily Cowin Changlong Wang Stuart D. C. Walsh |
| author_sort | Emily Cowin |
| collection | DOAJ |
| description | Offshore wind farms are a current area of interest in Australia due to their ability to support its transition to renewable energy. Climate reanalysis datasets that provide simulated wind speed data are frequently used to evaluate the potential of proposed offshore wind farm locations. However, there has been a lack of comparative studies of the accuracy of wind speed predictions from different reanalysis datasets for offshore wind farms in Australian waters. This paper assesses wind speed distribution accuracy and compares predictions of offshore wind turbine power output in Australia from three international reanalysis datasets: BARRA, ERA5, and MERRA-2. Pressure level data were used to determine wind speeds and capacity factors were calculated using a turbine bounding curve. Predictions across the datasets show consistent spatial and temporal variations in the predicted plant capacity factors, but the magnitudes differ substantially. Compared to weather station data, wind speed predictions from the BARRA dataset were found to be the most accurate, with a higher correlation and lower average error than ERA5 and MERRA-2. Significant variation was seen in predictions and there was a lack of similarity with weather station measurements, which highlights the need for additional site-based measurements. |
| first_indexed | 2024-03-11T05:04:09Z |
| format | Article |
| id | doaj.art-895ba86f8399420f940bd5367b727a6b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T05:04:09Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-895ba86f8399420f940bd5367b727a6b2023-11-17T19:04:44ZengMDPI AGEnergies1996-10732023-04-01168340410.3390/en16083404Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis DatasetsEmily Cowin0Changlong Wang1Stuart D. C. Walsh2Civil Engineering, Monash University, Clayton 3800, AustraliaCivil Engineering, Monash University, Clayton 3800, AustraliaCivil Engineering, Monash University, Clayton 3800, AustraliaOffshore wind farms are a current area of interest in Australia due to their ability to support its transition to renewable energy. Climate reanalysis datasets that provide simulated wind speed data are frequently used to evaluate the potential of proposed offshore wind farm locations. However, there has been a lack of comparative studies of the accuracy of wind speed predictions from different reanalysis datasets for offshore wind farms in Australian waters. This paper assesses wind speed distribution accuracy and compares predictions of offshore wind turbine power output in Australia from three international reanalysis datasets: BARRA, ERA5, and MERRA-2. Pressure level data were used to determine wind speeds and capacity factors were calculated using a turbine bounding curve. Predictions across the datasets show consistent spatial and temporal variations in the predicted plant capacity factors, but the magnitudes differ substantially. Compared to weather station data, wind speed predictions from the BARRA dataset were found to be the most accurate, with a higher correlation and lower average error than ERA5 and MERRA-2. Significant variation was seen in predictions and there was a lack of similarity with weather station measurements, which highlights the need for additional site-based measurements.https://www.mdpi.com/1996-1073/16/8/3404renewable resource estimationenergy transitionnumerical analysisoffshore wind |
| spellingShingle | Emily Cowin Changlong Wang Stuart D. C. Walsh Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets Energies renewable resource estimation energy transition numerical analysis offshore wind |
| title | Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets |
| title_full | Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets |
| title_fullStr | Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets |
| title_full_unstemmed | Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets |
| title_short | Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets |
| title_sort | assessing predictions of australian offshore wind energy resources from reanalysis datasets |
| topic | renewable resource estimation energy transition numerical analysis offshore wind |
| url | https://www.mdpi.com/1996-1073/16/8/3404 |
| work_keys_str_mv | AT emilycowin assessingpredictionsofaustralianoffshorewindenergyresourcesfromreanalysisdatasets AT changlongwang assessingpredictionsofaustralianoffshorewindenergyresourcesfromreanalysisdatasets AT stuartdcwalsh assessingpredictionsofaustralianoffshorewindenergyresourcesfromreanalysisdatasets |