An overview of wind power probabilistic forecasts

Over the past one to two decades, probabilistic forecasts have become more popular. Probabilistic forecasts take uncertainty into account and predict a probability distribution function (pdf). This study provides an overview of state-of-the-art technologies on wind-power probabilistic forecasts, and describes the fundamental concepts on those forecasting methods. Additionally, this study also summarizes various methods to evaluate the performance of a probabilistic forecast model. Finally, this study presents a case study using the ensemble prediction model, in which the wind-speed data were provided by the numerical weather prediction system (NWP) of the Central Weather Bureau (CWB) of Taiwan.

[1]  Henrik Madsen,et al.  Properties of quantile and interval forecasts of wind generation and their evaluation. , 2006 .

[2]  M. O'Malley,et al.  A new approach to quantify reserve demand in systems with significant installed wind capacity , 2005, IEEE Transactions on Power Systems.

[3]  Miao He,et al.  A Spatio-Temporal Analysis Approach for Short-Term Forecast of Wind Farm Generation , 2014, IEEE Transactions on Power Systems.

[4]  Georges Kariniotakis,et al.  Probabilistic short-term wind power forecasting based on kernel density estimators , 2007 .

[5]  J. M. Sloughter,et al.  Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging , 2010 .

[6]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[7]  Kit Po Wong,et al.  Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.

[8]  Amir F. Atiya,et al.  Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals , 2011, IEEE Transactions on Neural Networks.

[9]  Bri-Mathias Hodge,et al.  Wind power forecasting error distributions over multiple timescales , 2011, 2011 IEEE Power and Energy Society General Meeting.

[10]  Abbas Khosravi,et al.  Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Yonggang Wu,et al.  An Advanced Approach for Construction of Optimal Wind Power Prediction Intervals , 2015, IEEE Transactions on Power Systems.

[12]  Zhao Xu,et al.  Towards a Danish power system with 50% wind — Smart grids activities in denmark , 2009, 2009 IEEE Power & Energy Society General Meeting.

[13]  Pierre Pinson,et al.  Non‐parametric probabilistic forecasts of wind power: required properties and evaluation , 2007 .

[14]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[15]  Kit Po Wong,et al.  An overview on wind power forecasting methods , 2015, 2015 International Conference on Machine Learning and Cybernetics (ICMLC).

[16]  R. Buizza,et al.  Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models , 2009, IEEE Transactions on Energy Conversion.

[17]  Tom Heskes,et al.  Practical Confidence and Prediction Intervals , 1996, NIPS.

[18]  S. Tewari,et al.  A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets , 2011, IEEE Transactions on Power Systems.

[19]  S. Nahavandi,et al.  Prediction Intervals for Short-Term Wind Farm Power Generation Forecasts , 2013, IEEE Transactions on Sustainable Energy.

[20]  Yang Zhang,et al.  Voltage stability of large-scale wind plants integrated in weak networks: An ERCOT case study , 2015, 2015 IEEE Power & Energy Society General Meeting.

[21]  H. Künsch.  The Jackknife and the Bootstrap for General Stationary Observations , 1989 .

[22]  Henrik Madsen,et al.  Wind power Ensemble forecasting , 2004 .

[23]  Ulrich Focken,et al.  Short-term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects , 2002 .

[24]  Saeid Nahavandi,et al.  Combined Nonparametric Prediction Intervals for Wind Power Generation , 2013, IEEE Transactions on Sustainable Energy.

[25]  J.B. Theocharis,et al.  Long-term wind speed and power forecasting using local recurrent neural network models , 2006, IEEE Transactions on Energy Conversion.

[26]  Kit Po Wong,et al.  Optimal Prediction Intervals of Wind Power Generation , 2014, IEEE Transactions on Power Systems.

[27]  Kenneth Bruninx,et al.  A Statistical Description of the Error on Wind Power Forecasts for Probabilistic Reserve Sizing , 2014, IEEE Transactions on Sustainable Energy.

[28]  S. Watson,et al.  Application of wind speed forecasting to the integration of wind energy into a large scale power system , 1994 .

[29]  Yuan-Kang Wu,et al.  Integration of large-scale renewable power into the Taiwan power system , 2015 .

[30]  C. Chung,et al.  A Novel Probabilistic Optimal Power Flow Model With Uncertain Wind Power Generation Described by Customized Gaussian Mixture Model , 2016, IEEE Transactions on Sustainable Energy.

[31]  B. Zavadil,et al.  Grid impacts of wind power: a summary of recent studies in the United States , 2003 .

[32]  Kristin Dietrich,et al.  Stochastic Unit Commitment Considering Uncertain Wind Production: Application to an isolated system , 2009 .

[33]  Saeid Nahavandi,et al.  A New Fuzzy-Based Combined Prediction Interval for Wind Power Forecasting , 2016, IEEE Transactions on Power Systems.

[34]  Haiyan Liu,et al.  Wind energy resources exploitation and large-scale, non-grid-connection wind-powered industrial bases, construction in China Northwest Territories , 2009, 2009 World Non-Grid-Connected Wind Power and Energy Conference.

[35]  Pierre Pinson,et al.  Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models , 2005 .

[36]  J. Clarke,et al.  Integration of large scale wind generation in the New Zealand power system and electricity market , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[37]  N. Menemenlis,et al.  Computation of Dynamic Operating Balancing Reserve for Wind Power Integration for the Time-Horizon 1–48 Hours , 2012, IEEE Transactions on Sustainable Energy.

[38]  Jin Lin,et al.  A Versatile Probability Distribution Model for Wind Power Forecast Errors and Its Application in Economic Dispatch , 2013, IEEE Transactions on Power Systems.

[39]  Nigel Meade,et al.  Prediction intervals for growth curve forecasts , 1995 .

[40]  P Pinson,et al.  Conditional Prediction Intervals of Wind Power Generation , 2010, IEEE Transactions on Power Systems.

[41]  Henrik Madsen,et al.  Probabilistic Forecasts of Wind Power Generation Accounting for Geographically Dispersed Information , 2014, IEEE Transactions on Smart Grid.