A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting
By Yun Ju, Guangyu Sun, Quanhe Chen, Min Zhang, Huixian Zhu, Mujeeb Ur Rehman. IEEE Access, 2019
333 citations recorded by the source corpus.
The volatility and uncertainty of wind power often affect the quality of electric energy, the security of the power grid, the stability of the power system, and the fluctuation of the power market. In this case, the research on wind power forecasting is of great significance for ensuring the better development of wind power grids and the higher quality of electric energy. Therefore, a lot of new forecasting methods have been put forward. In this paper, a new forecasting model based on a convolution neural network and LightGBM is constructed. The procedure is shown as follows. First, we construct new feature sets by analyzing the characteristics of the raw data on the time series from the wind field and adjacent wind field. Second, the convolutional neural network (CNN) is proposed to extract information from input data, and the network parameters are adjusted by comparing the actual results. Third, in consideration of the limitations of the single-convolution model in predicting wind power, we innovatively integrated the LightGBM classification algorithm at the model to improve the forecasting accuracy and robustness. Finally, compared with the existing support vector machines, LightGBM, and CNN, the fusion model has better performance in accuracy and efficiency.
Subfields: Building and Construction, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/4c8364048a0efc4aeed07254d27d68ae22e5e622
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Building and Construction
In Computer Vision and Pattern Recognition
In Electrical and Electronic Engineering
Citations in the corpus
- A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting (2021, background): “LGBM enhances the capabilities of Gradient Boosted Decision Trees (GBDT) models in terms of running time acceleration and mitigation of memory consumptionwhile conserving a high accuracy [21].”
- A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects (2022, methodology): “Meanwhile, the LightGBM has been applied for different 547 classification problems, achieving excellent results [94], [95], 548 [96], [97], and its procedure is presented in Algorithm 3.”