Abstract
Renewable energy represented by wind energy plays an increasingly important role in China’s national energy system. The accurate prediction of wind power generation is of great significance to China’s energy planning and power grid dispatch. However, due to the late development of the wind power industry in China and the lag of power enterprise information, there are little historical data available at present. Therefore, the traditional large sample prediction method is difficult to be applied to the forecasting of wind power generation in China. For this kind of small sample and poor information problem, the grey prediction method can give a good solution. Thus, given the seasonal and long memory characteristics of the seasonal wind power generation, this paper constructs a seasonal discrete grey prediction model based on collaborative optimization. On the one hand, the model is based on moving average filtering algorithm to realize the recognition of seasonal and trend features. On the other hand, based on the optimization of fractional order and initial value, the collaborative optimization of trend and season is realized. To verify the practicability and accuracy of the proposed model, this paper uses the model to predict the quarterly wind power generation of China from 2012Q1 to 2020Q1, and compares the prediction results with the prediction results of the traditional GM(1,1) model, SGM(1,1) model and Holt-Winters model. The results are shown that the proposed model has a strong ability to capture the trend and seasonal fluctuation characteristics of wind power generation. And the long-term forecasts are valid if the existing wind power expansion capacity policy is maintained in the next four years. Based on the forecast of China’s wind power generation from 2021Q2 to 2024Q2 in the future, it is predicted that China’s wind power generation will reach 239.09 TWh in the future, which will be beneficial to the realization of China’s energy-saving and emission reduction targets.
Highlights
Abedinia et al realized the prediction of wind power by inputting the signal decomposed by improved empirical mode decomposition (IEMD) into a hybrid model based on bagging neural network combined with K-means clustering [1]
Most of the seasonal grey prediction models have been improved in capturing the seasonal and trend characteristics, the optimization of the seasonal grey prediction model, such as the selection of fractional order and the optimization of GM(1,1) model, lacks the comprehensive consideration of the seasonal and trend characteristics of the time series. To solve these problems, based on the seasonal and long memory characteristics of the seasonal wind power generation series, this paper adopts the moving average filtering algorithm and the Particle swarm optimization to establish the seasonal grey prediction model based on collaborative optimization (COSGM)
On the whole, starting from the seasonality of wind power generation, this paper proposes a new seasonal grey forecasting model based on collaborative optimization to excavate the development patterns of quarterly time series
Summary
The characteristics of small sample sizes and poor information of China’s wind power generation limit the application of prediction methods based on large sample sizes. Grey system theory is an effective method for modeling systems composed of small sample sizes that contain a limited amount of information This theory was proposed by Deng [25] in 1982 and has been widely used in the energy field. How to deal with the seasonal and long memory characteristics of the seasonal wind power capacity and improve the forecasting performance of the grey forecasting model becomes a key issue To solve this problem, a new seasonal discrete grey prediction model is proposed in this paper. Compared with the traditional seasonal grey forecasting model, this model captures seasonal characteristics of the seasonal wind power generation and can be applied to the data series of different periods
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