Probability Forecasting of Wind Power Ramp Events Using a Time Series Similarity Search Algorithm

2018 ◽  
Author(s):  
Bo Cao ◽  
Liuchen Chang ◽  
Xun Gong ◽  
Julian L Cardenas Barrera ◽  
Thomas Levy ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Similarity Search ◽  
Search Algorithm ◽  
Probability Forecasting ◽  
Ramp Events ◽  
Wind Power Ramp Events

scholarly journals Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

2011 ◽  
Vol 6 (1) ◽  
pp. 55-58 ◽  
Author(s):  
C. Gallego ◽  
A. Costa ◽  
A. Cuerva
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Regime Switching ◽  
Neural Nets ◽  
Ann Model ◽  
Short Term ◽  
Explanatory Variables ◽  
Ramp Events ◽  
Artificial Neural ◽  
Short Term Forecasting

Abstract. Ramp events are large rapid variations within wind power time series. Ramp forecasting can benefit from specific strategies so as to particularly take into account these shifts in the wind power output dynamic. In the short-term context (characterized by prediction horizons from minutes to a few days), a Regime-Switching (RS) model based on Artificial Neural Nets (ANN) is proposed. The objective is to identify three regimes in the wind power time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessment methodology is also proposed, based on a local gradient criterion. The RS-ANN model is compared to a single-ANN model (without regime discrimination), concluding that the regime-switching strategy leads to significant improvements for one-hour ahead forecasts, mainly due to the improvements obtained during ramp-up events. Including other explanatory variables (NWP outputs, local measurements) during the regime assessment could eventually improve forecasts for further horizons.

Multi-task learning for the prediction of wind power ramp events with deep neural networks

2020 ◽  
Vol 123 ◽  
pp. 401-411 ◽  
Author(s):  
M. Dorado-Moreno ◽  
N. Navarin ◽  
P.A. Gutiérrez ◽  
L. Prieto ◽  
A. Sperduti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Wind Power ◽  
Deep Neural Networks ◽  
Task Learning ◽  
Ramp Events ◽  
Wind Power Ramp Events

Robust estimation of wind power ramp events with reservoir computing

2017 ◽  
Vol 111 ◽  
pp. 428-437 ◽  
Author(s):  
M. Dorado-Moreno ◽  
L. Cornejo-Bueno ◽  
P.A. Gutiérrez ◽  
L. Prieto ◽  
C. Hervás-Martínez ◽  
...  
Keyword(s):  
Wind Power ◽  
Robust Estimation ◽  
Reservoir Computing ◽  
Ramp Events ◽  
Wind Power Ramp Events

A WT-ARMA based method for wind power ramp events forecasting

2016 ◽  
Author(s):  
Yongqian Liu Yongqian Liu ◽  
Ying Sun Ying Sun ◽  
Shuang Han Shuang Han ◽  
Yu Zhao Yu Zhao ◽  
Yong Zhao Yong Zhao ◽  
...  
Keyword(s):  
Wind Power ◽  
Ramp Events ◽  
Wind Power Ramp Events

scholarly journals Probabilistic Estimation of Wind Power Ramp Events: A Data-Driven Optimization Approach

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 23261-23269 ◽  
Author(s):  
Yang Cao ◽  
Wei Wei ◽  
Cheng Wang ◽  
Shengwei Mei ◽  
Shaowei Huang ◽  
...  
Keyword(s):  
Wind Power ◽  
Data Driven ◽  
Optimization Approach ◽  
Probabilistic Estimation ◽  
Ramp Events ◽  
Wind Power Ramp Events

scholarly journals Wind Power Ramp Event Forecasting Based on Feature Extraction and Deep Learning

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6449
Author(s):  
Li Han ◽  
Yan Qiao ◽  
Mengjie Li ◽  
Liping Shi
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Power Systems ◽  
Wind Power ◽  
Short Term Memory ◽  
Forecast Model ◽  
Forecast Performance ◽  
Model Based ◽  
Ramp Events ◽  
Wind Power Ramp Events

In order to improve the accuracy of wind power ramp forecasting and reduce the threat of ramps to the safe operation of power systems, a wind power ramp event forecast model based on feature extraction and deep learning is proposed in this work. Firstly, the Optimized Swinging Door Algorithm (OpSDA) is introduced to detect wind power ramp events, and the extraction results of ramp features, such as the ramp rate, are obtained. Then, a ramp forecast model based on a deep learning network is established. The historical wind power and its ramp features are used as the input of the forecast model, thereby strengthening the model’s learning for ramp features and preventing ramp features from being submerged in the complex wind power signal. A Convolutional Neural Network (CNN) is adopted to extract features from model inputs to obtain the coupling relationship between wind power and ramp features, and Long Short-Term Memory (LSTM) is utilized to learn the time-series relationship of the data. The forecast wind power is used as the output of the model, based on which the ramp forecast result is obtained after the ramp detection. Finally, the wind power data from the Elia website is used to verify the forecast performance of the proposed method for wind power ramp events.

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