Abstract
Compared to the onshore, the offshore wind farms have higher capacity factors. These high-capacity factors of the offshore wind farms and different wind conditions on the sea require new innovations to ensure a secure electricity grid. Wind power forecasting is indispensable to improve both the penetration of the wind energy in the energy mix and the economical and technical integration of a large share of the wind energy.
This study aims to represent a roadmap to develop wind power forecasting models for new offshore wind farms, for which no or limited power data are available. It investigates the development of wind power prediction quality of new offshore wind farms from planning to operation. This investigation represents improvement of forecast models for the first German offshore wind farm “alpha ventus.” The work is carried out with measured data from meteorological measurement mast Fino1, measured power from “alpha ventus,” and numerical weather predictions (NWP) from German Weather Service (DWD). Briefly summarized, this study aims to investigate development of forecast models for new offshore wind farms and to research reduction of prediction error via available historical data.
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Abbreviations
- ANN:
-
Artificial neural network
- C t :
-
Thrust coefficient
- DWD:
-
German weather service
- EWEA:
-
European Wind Energy Association
- IPCC:
-
Intergovernmental Panel on Climate Change
- ME:
-
Mixture of experts
- MOS:
-
Model output statistics
- MW:
-
Megawatt
- NNS:
-
Nearest-neighbor search
- nRMSE:
-
Normalized root mean square error
- NWP:
-
Numerical weather prediction
- RMSE:
-
Root mean square error
- SVM:
-
Support vector machines
- TSO:
-
Transmission system operator
- WCMS:
-
Wind farm cluster management system
- WPMS:
-
Wind power management system
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Kurt, M., Dobschinski, J., Lange, B., Wessel, A. (2017). From Planning to Operation: Wind Power Forecasting Model for New Offshore Wind Farms. In: Uyar, T. (eds) Towards 100% Renewable Energy. Springer Proceedings in Energy. Springer, Cham. https://doi.org/10.1007/978-3-319-45659-1_11
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DOI: https://doi.org/10.1007/978-3-319-45659-1_11
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