A Critical Review of Wind Power Forecasting Methods—Past, Present and Future
The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power fo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/15/3764 |
| _version_ | 1797561717616541696 |
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| author | Shahram Hanifi Xiaolei Liu Zi Lin Saeid Lotfian |
| author_facet | Shahram Hanifi Xiaolei Liu Zi Lin Saeid Lotfian |
| author_sort | Shahram Hanifi |
| collection | DOAJ |
| description | The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature. |
| first_indexed | 2024-03-10T18:17:45Z |
| format | Article |
| id | doaj.art-3e38fd11fd7d4bc39e870b492f4e5462 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T18:17:45Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3e38fd11fd7d4bc39e870b492f4e54622023-11-20T07:32:10ZengMDPI AGEnergies1996-10732020-07-011315376410.3390/en13153764A Critical Review of Wind Power Forecasting Methods—Past, Present and FutureShahram Hanifi0Xiaolei Liu1Zi Lin2Saeid Lotfian3James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKDepartment of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UKDepartment of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UKThe largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.https://www.mdpi.com/1996-1073/13/15/3764wind power forecastingartificial neural networkshybrid methodsperformance evaluation |
| spellingShingle | Shahram Hanifi Xiaolei Liu Zi Lin Saeid Lotfian A Critical Review of Wind Power Forecasting Methods—Past, Present and Future Energies wind power forecasting artificial neural networks hybrid methods performance evaluation |
| title | A Critical Review of Wind Power Forecasting Methods—Past, Present and Future |
| title_full | A Critical Review of Wind Power Forecasting Methods—Past, Present and Future |
| title_fullStr | A Critical Review of Wind Power Forecasting Methods—Past, Present and Future |
| title_full_unstemmed | A Critical Review of Wind Power Forecasting Methods—Past, Present and Future |
| title_short | A Critical Review of Wind Power Forecasting Methods—Past, Present and Future |
| title_sort | critical review of wind power forecasting methods past present and future |
| topic | wind power forecasting artificial neural networks hybrid methods performance evaluation |
| url | https://www.mdpi.com/1996-1073/13/15/3764 |
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