Abstract
Reducing the costs of wind power requires reasonable wind farm operation and maintenance strategies, and then to develop these strategies, the 24-hour ahead forecasting of wind speed is necessary. However, existing prediction work is mostly limited to 5 hours. This work developed a diurnal forecasting methodology for the regional wind farm according to real-life data of the supervisory control and data acquisition (SCADA) system of a wind farm from Jiangxi Province. The methodology used the variational mode decomposition (VMD) to extract wind characteristics, and then, the characteristics were put in the nonlinear autoregressive neural network (Narnet) and long short-term memory network (LSTM) for prediction; the forecast results of VMD-Narnet and VMD-LSTM are compared with the actual wind speed. The comparison results indicate that compared with the LSTM, the Narnet improves the accuracy by 61.90% in 24 hours on wind speed forecasting, and the predicted time horizon was improved by 6.8 hours. This work strongly supports the development of wind farm operation and maintenance strategies and provides a foundation for the reduction of wind power costs.
Highlights
For the requirement of cost reduction of wind farms, this work proposed a hybrid model of the variational mode decomposition (VMD) and the nonlinear autoregressive neural network (Narnet) on diurnal wind speed forecasting and compared the prediction of VMD-Narnet with the counterpart of VMDLSTM
The main contributions of our work are as follows: (1) A system framework for daily wind speed prediction based on supervisory control and data acquisition (SCADA) data is proposed, which ensures that each wind turbine has its wind speed prediction and the targeted operation and maintenance of each wind turbine
The effects of empirical mode decomposition (EMD) are widespread, the signal decomposition process still lacks mathematical theory to explain, which is the advantage of the VMD. e signal decomposition is built for the variational constraint model by VMD, and the introduction of the Lagrange multiplier transforms the constraint model to a nonconstrained variational problem
Summary
For the requirement of cost reduction of wind farms, this work proposed a hybrid model of the VMD and the nonlinear autoregressive neural network (Narnet) on diurnal wind speed forecasting and compared the prediction of VMD-Narnet with the counterpart of VMDLSTM. (3) Validation of the neural network with time variable can better predict wind speed, and the Narnet model has a prediction accuracy of 61.9% higher than the LSTM under the same input.
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