Evaluating the quality of scenarios of short-term wind power generation

Applied Energy ◽

10.1016/j.apenergy.2011.11.004 ◽

2012 ◽

Vol 96 ◽

pp. 12-20 ◽

Author(s):

P. Pinson ◽

R. Girard

Keyword(s):

Power Generation ◽

Wind Power Generation ◽

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Very short-term forecasting of wind power generation using hybrid deep learning model

Journal of Cleaner Production ◽

10.1016/j.jclepro.2021.126564 ◽

2021 ◽

Vol 296 ◽

pp. 126564

Author(s):

Md Alamgir Hossain ◽

Ripon K. Chakrabortty ◽

Sondoss Elsawah ◽

Michael J. Ryan

Keyword(s):

Power Generation ◽

Deep Learning ◽

Learning Model ◽

Wind Power Generation ◽

Short Term Forecasting ◽

Deep Learning Model

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Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network

Global Energy Interconnection ◽

10.1016/j.gloei.2021.01.003 ◽

2020 ◽

Vol 3 (6) ◽

pp. 571-576 ◽

Author(s):

Rui Yin ◽

Dengxuan Li ◽

Yifeng Wang ◽

Weidong Chen

Keyword(s):

Neural Network ◽

Power Generation ◽

Climate Model ◽

Short Term Memory ◽

Wind Power Generation ◽

Term Memory ◽

Forecasting Method ◽

Long Short Term Memory

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Short-term Power Forecast of Wind Power Generation Based on Genetic Algorithm Optimized Neural Network

Journal of Physics Conference Series ◽

10.1088/1742-6596/1601/2/022046 ◽

2020 ◽

Vol 1601 ◽

pp. 022046

Author(s):

Canzong Zhou ◽

Bin Tang ◽

Wei Cui ◽

Zhengmao Yao

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Power Generation ◽

Wind Power Generation ◽

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Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods

Energy Conversion and Management ◽

10.1016/j.enconman.2016.01.023 ◽

2016 ◽

Vol 112 ◽

pp. 208-219 ◽

Author(s):

Yachao Zhang ◽

Kaipei Liu ◽

Liang Qin ◽

Xueli An

Keyword(s):

Machine Learning ◽

Power Generation ◽

Wind Power Generation ◽

Variational Mode Decomposition ◽

Learning Methods ◽

Interval Prediction ◽

Machine Learning Methods ◽

Mode Decomposition

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Robust short-term prediction of wind power generation under uncertainty via statistical interpretation of multiple forecasting models

Energy ◽

10.1016/j.energy.2019.05.101 ◽

2019 ◽

Vol 180 ◽

pp. 387-397 ◽

Author(s):

Natapol Korprasertsak ◽

Thananchai Leephakpreeda

Keyword(s):

Power Generation ◽

Wind Power Generation ◽

Statistical Interpretation ◽

Forecasting Models ◽

Term Prediction ◽

Short Term Prediction

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Short-term forecasting of wind power generation using extreme learning machine and its variants

International Journal of Power and Energy Conversion ◽

10.1504/ijpec.2017.10001286 ◽

2017 ◽

Vol 8 (1) ◽

pp. 68

Author(s):

Sthita Prajna Mishra

Keyword(s):

Power Generation ◽

Extreme Learning Machine ◽

Wind Power Generation ◽

Short Term Forecasting ◽

Learning Machine

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Discussion of “Prediction Intervals for Short-Term Wind Farm Generation Forecasts” and “Combined Nonparametric Prediction Intervals for Wind Power Generation”

IEEE Transactions on Sustainable Energy ◽

10.1109/tste.2014.2323851 ◽

2014 ◽

Vol 5 (3) ◽

pp. 1019-1020 ◽

Author(s):

Pierre Pinson ◽

Julija Tastu

Keyword(s):

Power Generation ◽

Wind Farm ◽

Wind Power Generation ◽

Prediction Intervals ◽

Nonparametric Prediction ◽

Nonparametric Prediction Intervals

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The Study of Short-Term Wind Power Generation Prediction Methods Based on Clustering Analysis in Wind Farm

Sustainable Energy ◽

10.12677/se.2015.53003 ◽

2015 ◽

Vol 05 (03) ◽

pp. 17-23

Author(s):

存董

Keyword(s):

Power Generation ◽

Clustering Analysis ◽

Wind Farm ◽

Wind Power Generation ◽

Prediction Methods ◽

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Hourly-averaged solar plus wind power generation for Germany 2016: Long-term prediction, short-term forecasting, data mining and outlier analysis

Sustainable Cities and Society ◽

10.1016/j.scs.2020.102227 ◽

2020 ◽

Vol 60 ◽

pp. 102227

Author(s):

David A. Wood

Keyword(s):

Data Mining ◽

Power Generation ◽

Wind Power Generation ◽

Outlier Analysis ◽

Term Prediction ◽

Short Term Forecasting ◽

Long Term Prediction

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Short-Term Wind Power Generation Forecasting Based on the SVM-GM Approach

Electric Power Components and Systems ◽

10.1080/15325008.2018.1488188 ◽

2018 ◽

Vol 46 (11-12) ◽

pp. 1250-1264 ◽

Author(s):

Dinghui Wu ◽

Cong Gao

Keyword(s):

Power Generation ◽

Wind Power Generation ◽

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