| Abstract: |
| Environmental considerations have prompted the use of renewable energy resources worldwide for reduction of
greenhouse gas emissions. An accurate prediction of wind speed plays a major role in environmental planning,
energy system balancing, wind farm operation and control, power system planning, scheduling, storage capacity
optimization, and enhancing system reliability. This paper proposes an accurate prediction of wind speed based
ona Recursive Radial Basis Function Neural Network (RRBFNN) possessing the three inputs of wind direction,
temperature and wind speed to improve modern power system protection, control and management. Simulation
results confirm that the proposed model improves the wind speed prediction accuracy with least error when
compared with other existing prediction models. |
| Key words: Recursive radial basis function neural network, Prediction, Horizons, Generic, Wind speed |
| DOI：10.1186/s41601-020-00166-8 |
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| Fund: |
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