An Ensemble Model for Short-Term Wind Power Forecasting using Deep Learning and Gradient Boosting Algorithms

2020 ◽  
Author(s):  
Devesh Kumar ◽  
Rishabh Abhinav ◽  
Naran Pindoriya
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Gradient Boosting ◽  
Ensemble Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Boosting Algorithms ◽  
Power Forecasting

scholarly journals A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting

2019 ◽  
Vol 9 (15) ◽  
pp. 3019 ◽  
Author(s):  
Huan Zheng ◽  
Yanghui Wu
Keyword(s):  
Wind Power ◽  
Gradient Boosting ◽  
Support Vector ◽  
Similarity Analysis ◽  
Feature Engineering ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
Power Forecasting

Large-scale wind power access may cause a series of safety and stability problems. Wind power forecasting (WPF) is beneficial to dispatch in advance. In this paper, a new extreme gradient boosting (XGBoost) model with weather similarity analysis and feature engineering is proposed for short-term wind power forecasting. Based on the similarity among historical days’ weather, k-means clustering algorithm is used to divide the samples into several categories. Additionally, we also create some time features and drop unimportant features through feature engineering. For each category, we make predictions using XGBoost. The results of the proposed model are compared with the back propagation neural network (BPNN) and classification and regression tree (CART), random forests (RF), support vector regression (SVR), and a single XGBoost model. It is shown that the proposed model produces the highest forecasting accuracy among all these models.

Short-term wind power forecasting based on Attention Mechanism and Deep Learning

2022 ◽  
Vol 206 ◽  
pp. 107776
Author(s):  
Bangru Xiong ◽  
Lu Lou ◽  
Xinyu Meng ◽  
Xin Wang ◽  
Hui Ma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Attention Mechanism ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Power Forecasting
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