Multi-horizon Scalable Wind Power Forecast System

2018 ◽  
pp. 317-325
Author(s):  
Camilo Valenzuela ◽  
Héctor Allende ◽  
Carlos Valle
Keyword(s):  
Wind Power ◽  
Forecast System ◽  
Wind Power Forecast

Study on the adaptability of day-ahead wind power forecast system for on-site use

2013 ◽  
Author(s):  
Man Xu ◽  
Zongxiang Lu ◽  
Ying Qiao ◽  
Ningbo Wang ◽  
Shiyuan Zhou ◽  
...  
Keyword(s):  
Wind Power ◽  
Forecast System ◽  
Wind Power Forecast ◽  
Site Use

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

Wind power forecast based on improved Long Short Term Memory network

Energy ◽  
2019 ◽  
Vol 189 ◽  
pp. 116300 ◽  
Author(s):  
Li Han ◽  
Huitian Jing ◽  
Rongchang Zhang ◽  
Zhiyu Gao
Keyword(s):  
Wind Power ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Forecast ◽  
Memory Network ◽  
Long Short Term Memory

Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch

2014 ◽  
Vol 5 (1) ◽  
pp. 511-520 ◽  
Author(s):  
Le Xie ◽  
Yingzhong Gu ◽  
Xinxin Zhu ◽  
Marc G. Genton
Keyword(s):  
Power System ◽  
Wind Power ◽  
Short Term ◽  
Look Ahead ◽  
Wind Power Forecast ◽  
Spatio Temporal

Optimal Operation Planning of Wind Farm Installed BESS Using Wind Power Forecast Data of Wind Turbine Generators Considering Forecast Error

2013 ◽  
Vol 14 (3) ◽  
pp. 207-218 ◽  
Author(s):  
Kazuki Ogimi ◽  
Shota Kamiyama ◽  
Michael Palmer ◽  
Atsushi Yona ◽  
Tomonobu Senju ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Wind Farm ◽  
Forecast Error ◽  
Energy Resource ◽  
Optimal Operation ◽  
Turbine Generators ◽  
Wind Turbine Generators ◽  
Wind Power Forecast ◽  
Forecast Data

Abstract In order to solve the problems of global warming and depletion of energy resource, renewable energy systems such as wind generation are getting attention. However, wind power fluctuates due to variation of wind speed, and it is difficult to perfectly forecast wind power. This paper describes a method to use power forecast data of wind turbine generators considering wind power forecast error for optimal operation. The purpose in this paper is to smooth the output power fluctuation of a wind farm and to obtain more beneficial electrical power for selling.

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