Development and Operational Status of Wind Power Forecasting System

2013 ◽  
Vol 133 (4) ◽  
pp. 366-372 ◽  
Author(s):  
Isao Aoki ◽  
Ryoichi Tanikawa ◽  
Nobuyuki Hayasaki ◽  
Mitsuhiro Matsumoto ◽  
Shigero Enomoto
Keyword(s):  
Wind Power ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

Wind Power Forecasting Based on the BP Neural Network

2013 ◽  
Vol 341-342 ◽  
pp. 1303-1307 ◽  
Author(s):  
Jian Dong Mao ◽  
Xiao Jing Zhang ◽  
Juan Li
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Engineering Application ◽  
Weather Forecast ◽  
Difficult Problem ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

Accurate short-term wind power forecasting has important significance to safety, stability and economy of power system dispatching and also it is a difficult problem in practical engineering application. In this paper, by use of the data of numerical weather forecast, such as wind speed, wind direction, temperature, relative humidity and pressure of atmosphere, a short-term wind power forecasting system based on BP neural network has been developed. For verifying the feasibility of the system, some experiments have been were carried out. The results show that the system is capable of predicting accurately the wind power of future 24 hours and the forecasting accuracy of 85.6% is obtained. The work of this paper has important engineering directive significance to the similar wind power forecasting system.

scholarly journals Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts

Wind Energy ◽  
2006 ◽  
Vol 9 (1-2) ◽  
pp. 95-108 ◽  
Author(s):  
Henrik Aalborg Nielsen ◽  
Henrik Madsen ◽  
Torben Skov Nielsen
Keyword(s):  
Quantile Regression ◽  
Wind Power ◽  
Wind Power Forecasting ◽  
Probabilistic Forecasts ◽  
Forecasting System ◽  
Power Forecasting

Development and Operational Status of Wind Power Forecasting System

2014 ◽  
Vol 189 (4) ◽  
pp. 22-29 ◽  
Author(s):  
Isao Aoki ◽  
Ryoichi Tanikawa ◽  
Nobuyuki Hayasaki ◽  
Mitsuhiro Matsumoto ◽  
Shigero Enomoto
Keyword(s):  
Wind Power ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

A Wind Power Forecasting System to Optimize Power Integration

2011 ◽  
Author(s):  
Sue Ellen Haupt ◽  
Gerry Wiener ◽  
Yubao Liu ◽  
Bill Myers ◽  
Juanzhen Sun ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Wind Power ◽  
Learning Algorithm ◽  
Wrf Model ◽  
Forecast Accuracy ◽  
The United States ◽  
Processing Technologies ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

The National Center for Atmospheric Research (NCAR) has developed a wind prediction system for Xcel Energy, the power company with the largest wind capacity in the United States. The wind power forecasting system includes advanced modeling capabilities, data assimilation, nowcasting, and statistical post-processing technologies. The system ingests both external model data and observations. NCAR produces a deterministic mesoscale wind forecast of hub height winds on a very fine resolution grid using the Weather Research and Forecasting (WRF) model, run using the Real Time Four Dimensional Data Assimilation (RTFDDA) system. In addition, a 30 member ensemble system is run to both improve forecast accuracy and provide an indication of forecast uncertainty. The deterministic and ensemble model output plus data from various global and regional models are ingested by NCAR’s Dynamic, Integrated, Forecast System (DICast®), a statistical learning algorithm. DICast® produces forecasts of wind speed for each wind turbine. These wind forecasts are then fed into a power conversion algorithm that has been empirically derived for each Xcel power connection node. In addition, a ramp forecasting technology fine-tunes the capability to accurately predict the time, magnitude, and duration of a ramping event. This basic system has consistently improved Xcel’s ability to optimize the economics of incorporating wind energy into their power system.

D110 Wind power forecasting system for the control area of power grid in Japan

2008 ◽  
Vol 2008.13 (0) ◽  
pp. 165-166
Author(s):  
Nobuyuki HAYASAKI ◽  
Isao AOKI ◽  
Ryoichi TANIKAWA ◽  
Hisashi FUKUDA
Keyword(s):  
Wind Power ◽  
Power Grid ◽  
Control Area ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

scholarly journals A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1372 ◽  
Author(s):  
Branko Kosovic ◽  
Sue Ellen Haupt ◽  
Daniel Adriaansen ◽  
Stefano Alessandrini ◽  
Gerry Wiener ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Extreme Events ◽  
Weather Prediction ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Forecasting System ◽  
Power Forecasting

The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.

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