Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network
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 pla...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-10-01
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| Series: | Protection and Control of Modern Power Systems |
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| Online Access: | http://link.springer.com/article/10.1186/s41601-020-00166-8 |
| Summary: | 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. |
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| ISSN: | 2367-2617 2367-0983 |