Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Short-term wind power prediction based on the combination of numerical weather forecast and time series
The accurate prediction of wind power has a huge impact on the grid connection and dispatching of the power system. In order to make the prediction accuracy of wind power higher, this paper proposes a combined forecasting model based on the combination of numerical weather prediction (NWP) and wind power time series, called gray wolf algorithm-wavelet neural network-variational mode decomposition-long short-term memory-Q-learning (GWO-WNN-VMD-LSTM-Q-learning). First, the wind power prediction (WPP) is implemented based on the NWP, and prediction result 1 is obtained. In this stage, the wavelet neural network (WNN), which is optimized by the gray wolf algorithm (GWO), is used for prediction. Then, the historical time series of wind power is subjected to variational mode decomposition (VMD), and the decomposed sub-sequences are predicted by long short-term memory (LSTM) networks, respectively, and the prediction results of each sub-sequence are summed to obtain the prediction result 2. Finally, the Q-learning algorithm is used to superimpose prediction result 1 and result 2 on the basis of optimal weight and get the final WPP results. The simulation results demonstrate that this model's prediction accuracy is high and that it has a substantially greater predictive impact than other traditional models that merely take time series or numerical weather forecasts into account.
Short-term wind power prediction based on the combination of numerical weather forecast and time series
The accurate prediction of wind power has a huge impact on the grid connection and dispatching of the power system. In order to make the prediction accuracy of wind power higher, this paper proposes a combined forecasting model based on the combination of numerical weather prediction (NWP) and wind power time series, called gray wolf algorithm-wavelet neural network-variational mode decomposition-long short-term memory-Q-learning (GWO-WNN-VMD-LSTM-Q-learning). First, the wind power prediction (WPP) is implemented based on the NWP, and prediction result 1 is obtained. In this stage, the wavelet neural network (WNN), which is optimized by the gray wolf algorithm (GWO), is used for prediction. Then, the historical time series of wind power is subjected to variational mode decomposition (VMD), and the decomposed sub-sequences are predicted by long short-term memory (LSTM) networks, respectively, and the prediction results of each sub-sequence are summed to obtain the prediction result 2. Finally, the Q-learning algorithm is used to superimpose prediction result 1 and result 2 on the basis of optimal weight and get the final WPP results. The simulation results demonstrate that this model's prediction accuracy is high and that it has a substantially greater predictive impact than other traditional models that merely take time series or numerical weather forecasts into account.
Short-term wind power prediction based on the combination of numerical weather forecast and time series
Zeng, Liang (Autor:in) / Lan, Xin (Autor:in) / Wang, Shanshan (Autor:in)
01.01.2023
24 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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