scholarly journals Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 159 ◽  
Author(s):  
Long Cai ◽  
Jie Gu ◽  
Jinghuan Ma ◽  
Zhijian Jin
Keyword(s):  
Power System ◽  
Decision Trees ◽  
Wind Power ◽  
Transfer Learning ◽  
Training Data ◽  
Gradient Boosting ◽  
Training Process ◽  
Wind Power Forecasting ◽  
Proposed Model ◽  
Power Forecasting

With the high wind penetration in the power system, accurate and reliable probabilistic wind power forecasting has become even more significant for the reliability of the power system. In this paper, an instance-based transfer learning method combined with gradient boosting decision trees (GBDT) is proposed to develop a wind power quantile regression model. Based on the spatial cross-correlation characteristic of wind power generations in different zones, the proposed model utilizes wind power generations in correlated zones as the source problems of instance-based transfer learning. By incorporating the training data of source problems into the training process, the proposed model successfully reduces the prediction error of wind power generation in the target zone. To prevent negative transfer, this paper proposes a method that properly assigns weights to data from different source problems in the training process, whereby the weights of related source problems are increased, while those of unrelated ones are reduced. Case studies are developed based on the dataset from the Global Energy Forecasting Competition 2014 (GEFCom2014). The results confirm that the proposed model successfully improves the prediction accuracy compared to GBDT-based benchmark models, especially when the target problem has a small training set while resourceful source problems are available.

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.

Insurance strategy for mitigating power system operational risk introduced by wind power forecasting uncertainty

2016 ◽  
Vol 89 ◽  
pp. 606-615 ◽  
Author(s):  
Hongming Yang ◽  
Jing Qiu ◽  
Ke Meng ◽  
Jun Hua Zhao ◽  
Zhao Yang Dong ◽  
...  
Keyword(s):  
Power System ◽  
Wind Power ◽  
Operational Risk ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals Probabilistic Short-Term Wind Power Forecasting Using Sparse Bayesian Learning and NWP

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Kaikai Pan ◽  
Zheng Qian ◽  
Niya Chen
Keyword(s):  
Wind Power ◽  
Bayesian Learning ◽  
Weather Prediction ◽  
Parameters Optimization ◽  
Gaussian Kernel ◽  
Sparse Bayesian Learning ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Proposed Model ◽  
Power Forecasting

Probabilistic short-term wind power forecasting is greatly significant for the operation of wind power scheduling and the reliability of power system. In this paper, an approach based on Sparse Bayesian Learning (SBL) and Numerical Weather Prediction (NWP) for probabilistic wind power forecasting in the horizon of 1–24 hours was investigated. In the modeling process, first, the wind speed data from NWP results was corrected, and then the SBL was used to build a relationship between the combined data and the power generation to produce probabilistic power forecasts. Furthermore, in each model, the application of SBL was improved by using modified-Gaussian kernel function and parameters optimization through Particle Swarm Optimization (PSO). To validate the proposed approach, two real-world datasets were used for construction and testing. For deterministic evaluation, the simulation results showed that the proposed model achieves a greater improvement in forecasting accuracy compared with other wind power forecast models. For probabilistic evaluation, the results of indicators also demonstrate that the proposed model has an outstanding performance.

scholarly journals New tool for integration of wind power forecasting into power system operation

2009 ◽  
Author(s):  
Andrej F. Gubina ◽  
Andrew Keane ◽  
Peter Meibom ◽  
Jonathan O'Sullivan ◽  
Oisin Goulding ◽  
...  
Keyword(s):  
Power System ◽  
Wind Power ◽  
System Operation ◽  
Power System Operation ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals Continuous and Distribution-free Probabilistic Wind Power Forecasting

2021 ◽  
Author(s):  
honglin wen ◽  
Pierre Pinson ◽  
jinghuan ma ◽  
jie gu ◽  
Zhijiang Jin
Keyword(s):  
Wind Power ◽  
Distribution Free ◽  
Wind Power Forecasting ◽  
Proposed Model ◽  
Probabilistic Forecasts ◽  
Probability Densities ◽  
Data Driven Approach ◽  
Open Datasets ◽  
Approximation Capability ◽  
Power Forecasting

We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow~(CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base distribution and a set of bijective mappings. Both the shape parameters of the base distribution and the bijective mappings are approximated with neural networks. Spline-based conditional normalizing flow is considered owing to its universal approximation capability. Over the training phase, the model sequentially maps input examples onto samples of base distribution, where parameters are estimated through maximum likelihood. To issue probabilistic forecasts, one eventually map samples of the base distribution into samples of a desired distribution. Case studies based on open datasets validate the effectiveness of the proposed model, and allows us to discuss its advantages and caveats with respect to the state of the art. Code will be released upon publication.

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