Abstract
Wind power plays a crucial role in the secure conversion and management of the power system. Therefore, this study proposes a hybrid model for short-term wind power forecasting, which consists of the variational mode decomposition(VMD), the K-means clustering algorithm and long short term memory(LSTM) network.The combination model is conducted as follows: the VMD decomposes the raw wind power series into a certain number of sub-layers with different frequencies; K-means as a data mining approach is executed for splitting the data into an ensemble of components with similar fluctuant level of each sub-layer; LSTM is adopted as the principal forecasting engine for capturing the unsteady characteristics of each component. Eventually, the forecasting results would be generated by aggregating the predicted components.To evaluate the fitting capacity of the proposed model, seven different models including the back propagation neural network(BP) approach, the Elman neural network(ELMAN), the LSTM approach, the VMD-BP approach, the VMD-Elman approach, the VMD-LSTM approach and the VMD-Kmeans-LSTM approach are implemented on four wind power series for multiple scales. The experimental results demonstrate the best performance in favour of the proposed model.
Highlights
With In keeping with the fact that the traditional fossil fuels are depleting with an irreversible trend, high share of renewable energy integration will be one of the basic characteristics of the future power system [1]
Inspired by the promising results in the field of computer version and speech signal processing [19], a novel model combining the improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) and grey wolves optimisation (GWO) algorithms was developed for short term wind speed forecasting
(5) To investigate the generalized ability of the provided VMD-Kmeans-LSTM approach, seven different approaches including the BP approach, the ELMAN approach, the LSTM approach, the VMD-BP approach, the VMD-Elman approach, the VMD-LSTM approach and the VMD-Kmeans-LSTM approach are employed in performance evaluation
Summary
With In keeping with the fact that the traditional fossil fuels are depleting with an irreversible trend, high share of renewable energy integration will be one of the basic characteristics of the future power system [1]. A hybrid model for wind power forecasting integrating the NWP method and an artificial neural network(ANN) was proposed to enhance the performance in the long horizon [7]. Inspired by the promising results in the field of computer version and speech signal processing [19], a novel model combining the improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) and grey wolves optimisation (GWO) algorithms was developed for short term wind speed forecasting. The significant drawback of the EMD is the frequent appearance of mode mixing, which creates the fuzziness of the intrinsic mode functions(IMFs) It can be seen from recent researches that data mining algorithms have received considerable attentions for forecasting wind speed and wind power. A novel hybrid model based on VMD decomposition, K-means clustering and LSTM principal computing is successfully present for predicting short-term wind power. (5) To investigate the generalized ability of the provided VMD-Kmeans-LSTM approach, seven different approaches including the BP approach, the ELMAN approach, the LSTM approach, the VMD-BP approach, the VMD-Elman approach, the VMD-LSTM approach and the VMD-Kmeans-LSTM approach are employed in performance evaluation
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