Wind speed and direction forecasting for wind power generation using ARIMA model
© 2017 IEEE. Wind Power plays a major role in both large utility grids and small microgrids due to a wide range of socio-economic benefits. Due to this reason, current research has an emerging trend to enhance its reliability and usability. Highly random nature of the wind speed and direction leads...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
IEEE
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/72615 |
| _version_ | 1848762797460029440 |
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| author | Yatiyana, E. Rajakaruna, Sumedha Ghosh, Arindam |
| author_facet | Yatiyana, E. Rajakaruna, Sumedha Ghosh, Arindam |
| author_sort | Yatiyana, E. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 IEEE. Wind Power plays a major role in both large utility grids and small microgrids due to a wide range of socio-economic benefits. Due to this reason, current research has an emerging trend to enhance its reliability and usability. Highly random nature of the wind speed and direction leads to having a poor accuracy of wind power forecasting and thereby poor reliability, increased cost and reduced efficiency of electrical systems. Most updated studies are focused mainly on wind speed, and their prediction errors are above the industry expectations. In this paper, both the wind speed and wind direction are analyzed to develop a statistical model based forecasting technique. This paper uses an Autoregressive Integrated Moving Average method to build the estimating model for wind measured in Western Australia to yield the forecasted values. The resultant model can be used to improve the system reliability, quality of the wind power generation system. |
| first_indexed | 2025-11-14T10:53:17Z |
| format | Conference Paper |
| id | curtin-20.500.11937-72615 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:53:17Z |
| publishDate | 2018 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-726152018-12-13T09:33:50Z Wind speed and direction forecasting for wind power generation using ARIMA model Yatiyana, E. Rajakaruna, Sumedha Ghosh, Arindam © 2017 IEEE. Wind Power plays a major role in both large utility grids and small microgrids due to a wide range of socio-economic benefits. Due to this reason, current research has an emerging trend to enhance its reliability and usability. Highly random nature of the wind speed and direction leads to having a poor accuracy of wind power forecasting and thereby poor reliability, increased cost and reduced efficiency of electrical systems. Most updated studies are focused mainly on wind speed, and their prediction errors are above the industry expectations. In this paper, both the wind speed and wind direction are analyzed to develop a statistical model based forecasting technique. This paper uses an Autoregressive Integrated Moving Average method to build the estimating model for wind measured in Western Australia to yield the forecasted values. The resultant model can be used to improve the system reliability, quality of the wind power generation system. 2018 Conference Paper http://hdl.handle.net/20.500.11937/72615 10.1109/AUPEC.2017.8282494 IEEE restricted |
| spellingShingle | Yatiyana, E. Rajakaruna, Sumedha Ghosh, Arindam Wind speed and direction forecasting for wind power generation using ARIMA model |
| title | Wind speed and direction forecasting for wind power generation using ARIMA model |
| title_full | Wind speed and direction forecasting for wind power generation using ARIMA model |
| title_fullStr | Wind speed and direction forecasting for wind power generation using ARIMA model |
| title_full_unstemmed | Wind speed and direction forecasting for wind power generation using ARIMA model |
| title_short | Wind speed and direction forecasting for wind power generation using ARIMA model |
| title_sort | wind speed and direction forecasting for wind power generation using arima model |
| url | http://hdl.handle.net/20.500.11937/72615 |