Abstract
Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.
Highlights
Environmental pollution and serious shortage of energy have become the most pressing problems in the world today
The contribution of this research consists mainly of two aspects: (1) establishing a national wind power generation forecasting model based on normalization and Wavelet Decomposition (WD)-Long Short-Term Memory neural network (LSTM); and (2) taking national macroeconomic indicators and related renewable energy power generation as input indicators, while the dimensionless data are realized through normalization and the data dimension is optimized by wavelet decomposition, which improves the convergence speed of the model and the prediction accuracy of the combined model
Wavelet Decomposition and Long Short-Term Memory neural network (WD-LSTM) is an intelligent network combining the advantages of WD and LSTM neural network
Summary
Environmental pollution and serious shortage of energy have become the most pressing problems in the world today. Macroeconomic indicators and related renewable energy generation are selected as input indicators of the prediction model, while wind power generation in China is taken as output indicators, which greatly reduces the randomness and uncertainty of input data and overcomes the limitation in previous studies of single wind farms being used as prediction objects. The contribution of this research consists mainly of two aspects: (1) establishing a national wind power generation forecasting model based on normalization and WD-LSTM; and (2) taking national macroeconomic indicators (gross domestic product, consumer price index, industrial added value and total imports and exports) and related renewable energy power generation (total power generation and hydropower generation) as input indicators, while the dimensionless data are realized through normalization and the data dimension is optimized by wavelet decomposition, which improves the convergence speed of the model and the prediction accuracy of the combined model
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