Characterizing future large, rapid changes in aggregated wind power using Numerical Weather Prediction spatial fields

Wind Energy ◽  
2009 ◽  
Vol 12 (6) ◽  
pp. 542-555 ◽  
Author(s):  
Nicholas J. Cutler ◽  
Hugh R. Outhred ◽  
Iain F. MacGill ◽  
Merlinde J. Kay ◽  
Jeffrey D. Kepert
Keyword(s):  
Wind Power ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Rapid Changes ◽  
Spatial Fields

scholarly journals Characterizing Wind Power Forecast Uncertainty with Numerical Weather Prediction Spatial Fields

2008 ◽  
Vol 32 (6) ◽  
pp. 509-524 ◽  
Author(s):  
Nicholas J. Cutler ◽  
Jeffrey D. Kepert ◽  
Hugh R. Outhred ◽  
Iain F. MacGill
Keyword(s):  
Wind Power ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Forecast Uncertainty ◽  
Numerical Weather ◽  
Wind Power Forecast ◽  
Spatial Fields

scholarly journals Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Lorenzo Donadio ◽  
Jiannong Fang ◽  
Fernando Porté-Agel
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Artificial Neural ◽  
Power Forecasting

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

scholarly journals Forecasting ramps of wind power production with numerical weather prediction ensembles

Wind Energy ◽  
2012 ◽  
Vol 16 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Arthur Bossavy ◽  
Robin Girard ◽  
George Kariniotakis
Keyword(s):  
Wind Power ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Power Production ◽  
Numerical Weather
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