A Short-Term Wind Power Forecasting Approach With Adjustment of Numerical Weather Prediction Input by Data Mining

2015 ◽  
Vol 6 (4) ◽  
pp. 1283-1291 ◽  
Author(s):  
Qianyao Xu ◽  
Dawei He ◽  
Ning Zhang ◽  
Chongqing Kang ◽  
Qing Xia ◽  
...  
Keyword(s):  
Data Mining ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Power Forecasting

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 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.

scholarly journals Probabilistic Short-Term Wind Power Forecasting Using Sparse Bayesian Learning and NWP

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Kaikai Pan ◽  
Zheng Qian ◽  
Niya Chen
Keyword(s):  
Wind Power ◽  
Bayesian Learning ◽  
Weather Prediction ◽  
Parameters Optimization ◽  
Gaussian Kernel ◽  
Sparse Bayesian Learning ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Proposed Model ◽  
Power Forecasting

Probabilistic short-term wind power forecasting is greatly significant for the operation of wind power scheduling and the reliability of power system. In this paper, an approach based on Sparse Bayesian Learning (SBL) and Numerical Weather Prediction (NWP) for probabilistic wind power forecasting in the horizon of 1–24 hours was investigated. In the modeling process, first, the wind speed data from NWP results was corrected, and then the SBL was used to build a relationship between the combined data and the power generation to produce probabilistic power forecasts. Furthermore, in each model, the application of SBL was improved by using modified-Gaussian kernel function and parameters optimization through Particle Swarm Optimization (PSO). To validate the proposed approach, two real-world datasets were used for construction and testing. For deterministic evaluation, the simulation results showed that the proposed model achieves a greater improvement in forecasting accuracy compared with other wind power forecast models. For probabilistic evaluation, the results of indicators also demonstrate that the proposed model has an outstanding performance.

Sign in / Sign up

Export Citation Format

Share Document