Short-Term Wind Power Prediction Approach Based On Bayesian Optimization and Ensemble Learning

Journal of Intelligent Systems: Theory and Applications ◽

10.38016/jista.889991 ◽

2021 ◽

pp. 142-154

Author(s):

Kübra YAZICI ◽

Semra BORAN

Keyword(s):

Ensemble Learning ◽

Bayesian Optimization ◽

Power Prediction ◽

Wind Power Prediction ◽

Prediction Approach

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A short-term wind power prediction approach based on the dynamic classification of the weather types of wind farms

2017 China International Electrical and Energy Conference (CIEEC) ◽

10.1109/cieec.2017.8388518 ◽

2017 ◽

Author(s):

Xiaosheng Peng ◽

Wenhan Fan ◽

Fan Yang ◽

Jianfeng Che ◽

Bo Wang

Keyword(s):

Power Prediction ◽

Weather Types ◽

Wind Power Prediction ◽

Prediction Approach

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A very short term wind power prediction approach based on Multilayer Restricted Boltzmann Machine

2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) ◽

10.1109/appeec.2016.7779917 ◽

2016 ◽

Author(s):

Xiaosheng Peng ◽

Lei Xiong ◽

Jinyu Wen ◽

Yuan Xu ◽

Wenhan Fan ◽

...

Keyword(s):

Restricted Boltzmann Machine ◽

Boltzmann Machine ◽

Power Prediction ◽

Wind Power Prediction ◽

Prediction Approach

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Short-Term Wind Power Prediction for Wind Turbine via Kalman Filter based on JIT Modeling

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.135.81 ◽

2015 ◽

Vol 135 (1) ◽

pp. 81-89 ◽

Author(s):

Tomoki Ishikawa ◽

Takaaki Kojima ◽

Toru Namerikawa

Keyword(s):

Kalman Filter ◽

Wind Turbine ◽

Power Prediction ◽

Wind Power Prediction

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A Short-Term Regional Wind Power Prediction Method Based on XGBoost and Multi-stage Features Selection

2020 IEEE 3rd Student Conference on Electrical Machines and Systems (SCEMS) ◽

10.1109/scems48876.2020.9352249 ◽

2020 ◽

Author(s):

Wenze Li ◽

Xiaosheng Peng ◽

Kai Cheng ◽

Hongyu Wang ◽

Qiyou Xu ◽

...

Keyword(s):

Prediction Method ◽

Features Selection ◽

Power Prediction ◽

Multi Stage ◽

Wind Power Prediction

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Short-Term Wind Power Prediction Based on Principal Component Analysis and Elman Artificial Neural Networks

2017 4th International Conference on Information Science and Control Engineering (ICISCE) ◽

10.1109/icisce.2017.146 ◽

2017 ◽

Author(s):

Shaung Hu ◽

Ke-Jun Li

Keyword(s):

Neural Networks ◽

Principal Component Analysis ◽

Artificial Neural Networks ◽

Principal Component ◽

Component Analysis ◽

Power Prediction ◽

Wind Power Prediction ◽

Artificial Neural

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Short-Term Wind Power Prediction Considering the Influence of Terrain

International Journal of Simulation Systems Science & Technology ◽

10.5013/ijssst.a.17.38.28 ◽

2016 ◽

Author(s):

Guest Editor Manhui Su

Keyword(s):

Power Prediction ◽

Wind Power Prediction

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A dynamic adaptive algorithm for statistical up-scaling based short-term wind power prediction of regions

2017 China International Electrical and Energy Conference (CIEEC) ◽

10.1109/cieec.2017.8388524 ◽

2017 ◽

Author(s):

Xiaosheng Peng ◽

Yuzhu Chen ◽

Jianfeng Che ◽

Bo Wang

Keyword(s):

Adaptive Algorithm ◽

Power Prediction ◽

Wind Power Prediction ◽

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Short Term Wind Power Prediction Based on Wavelet Transform and BP Neural Network

Green Energy and Networking - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ◽

10.1007/978-3-030-62483-5_26 ◽

2020 ◽

pp. 245-254

Author(s):

Shuang Zheng ◽

Zhaoju Jia ◽

Ziwei Zhang ◽

Fugang Liu ◽

Long Han

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Bp Neural Network ◽

Power Prediction ◽

Wind Power Prediction

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Short-Term Wind Power Prediction Based on Empirical Mode Decomposition and Extreme Learning Machine

Proceedings of the 2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics ◽

10.2991/emcpe-16.2016.173 ◽

2016 ◽

Author(s):

Jiajia Wu ◽

Changliang Liu

Keyword(s):

Extreme Learning Machine ◽

Empirical Mode Decomposition ◽

Power Prediction ◽

Mode Decomposition ◽

Wind Power Prediction ◽

Learning Machine

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Short-term Wind Power Prediction Based on Wavelet Feature Arrangement and Convolutional Neural Networks Deep Learning

IEEE Transactions on Industry Applications ◽

10.1109/tia.2021.3106887 ◽

2021 ◽

pp. 1-1

Author(s):

Xiaosheng Peng ◽

Yinhuan Li ◽

Lei Dong ◽

Kai Cheng ◽

Hongyu Wang ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Power Prediction ◽

Wind Power Prediction

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