|
In the context of carbon peaking and carbon neutralization, developing new energy such as wind generation greatly is an effective way to deal with global climate change and energy crisis. However, due to the intermittence and volatility of wind generation, there are many influencing factors affecting it, and the influence mechanism between them is complex, so it is difficult to predict the wind power with a high-precision. In order to improve the accuracy of wind power prediction, this paper proposes a forecasting method based on LassoLarIC and Long Short-Term Memory (LSTM) for wind power from the perspective of variable selection and feature extraction, so as to extract the best feature set of wind power from different influencing factors, different historical data and different leading steps. Firstly, the optimal leading steps are selected by LassoLarIC, so the leading steps with the highest correlation are found from the historical data; Secondly, LassoLarIC is used again to extract the features from the selected leading steps, so the impact of different influencing factors in different leading steps are evaluated effectively, and the optimal feature set of wind power are obtained accurately; Finally, based on the optimal feature set extracted by LassoLarIC, LSTM is used to predict the wind power in the next hour. The prediction and comparison results show that the effect of feature extraction is obvious, the prediction accuracy before and after feature extraction is improved by more than 25%. The conclusions of this study can provide reference for variable selection and feature extraction of wind power prediction research
|
