An overview on wind power forecasting methods

With the continually increasing growth in wind generation being integrated into the electric networks, it brings about significant challenges for decision-makers of power system operation due to its high volatility and uncertainty. One efficient approach to tackling such a problem is using reliable forecasting tools. As the conventional point forecasting can only provide a deterministic predicted value, instead, the probabilistic interval forecasting was attracted broad attention in the last few years since it can reflect the information of the uncertainties associated with wind power generation, which can significantly facilitate a large number of decision-making problems in power system operation. This paper presents an overview of current methods used in wind power forecasting. First of all, the frequently-used traditional point forecasting methods are reviewed Afterwards, various state-of-the-art techniques in terms of probabilistic forecasting are discussed. The indications for future development in wind power forecasting approaches and conclusions are given in the end.

[1]  Jin Lin,et al.  Versatile distribution of wind power output for a given forecast value , 2012, PES 2012.

[2]  Pierre Pinson,et al.  Discussion of “Prediction Intervals for Short-Term Wind Farm Generation Forecasts” and “Combined Nonparametric Prediction Intervals for Wind Power Generation” , 2014 .

[3]  H. Bludszuweit,et al.  A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty , 2011, IEEE Transactions on Power Systems.

[4]  Takashi Yasuno,et al.  Output prediction of wind power generation system using complex-valued neural network , 2010, Proceedings of SICE Annual Conference 2010.

[5]  M. Diagne,et al.  Review of solar irradiance forecasting methods and a proposition for small-scale insular grids , 2013 .

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

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

[8]  B D Stollar,et al.  Bacterial expression of anti-DNA antibody domains. , 1997, Methods.

[9]  N. D. Hatziargyriou,et al.  Probabilistic Wind Power Forecasting Using Radial Basis Function Neural Networks , 2012, IEEE Transactions on Power Systems.

[10]  Leonard A. Smith,et al.  Evaluating Probabilistic Forecasts Using Information Theory , 2002 .

[11]  Jian Ma,et al.  Incorporating Uncertainty of Wind Power Generation Forecast Into Power System Operation, Dispatch, and Unit Commitment Procedures , 2011, IEEE Transactions on Sustainable Energy.

[12]  Joao P. S. Catalao,et al.  A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal , 2011 .

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

[14]  Kit Po Wong,et al.  Discussion of “Combined Nonparametric Prediction Intervals for Wind Power Generation” , 2014 .

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

[16]  Zijun Zhang,et al.  Short-Horizon Prediction of Wind Power: A Data-Driven Approach , 2010, IEEE Transactions on Energy Conversion.

[17]  M. Lange.  On the Uncertainty of Wind Power Predictions—Analysis of the Forecast Accuracy and Statistical Distribution of Errors , 2005 .

[18]  Henrik Madsen,et al.  Ensemble-based Probabilistic Forecasting at Horns Rev , 2009 .

[19]  Robert P. Broadwater,et al.  Current status and future advances for wind speed and power forecasting , 2014 .

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

[21]  Mohammad Monfared,et al.  A new strategy for wind speed forecasting using artificial intelligent methods , 2009 .

[22]  Freyr Sverrisson,et al.  Renewables 2014 : global status report , 2014 .

[23]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[24]  Enrico Zio,et al.  A Genetic Algorithm and Neural Network Technique for Predicting Wind Power Under Uncertainty , 2013 .

[25]  G. Sideratos,et al.  Using Radial Basis Neural Networks to Estimate Wind Power Production , 2007, 2007 IEEE Power Engineering Society General Meeting.

[26]  P. Pinson,et al.  Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power , 2007, IEEE Transactions on Power Systems.

[27]  Kit Po Wong,et al.  A Hybrid Approach for Probabilistic Forecasting of Electricity Price , 2014, IEEE Transactions on Smart Grid.

[28]  Hongming Zhou,et al.  Optimization method based extreme learning machine for classification , 2010, Neurocomputing.

[29]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[30]  Henrik Madsen,et al.  Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts , 2006 .

[31]  Zhao Xu,et al.  Direct Interval Forecasting of Wind Power , 2013, IEEE Transactions on Power Systems.

[32]  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.

[33]  Joao P. S. Catalao,et al.  Application of adaptive neuro‐fuzzy inference for wind power short‐term forecasting , 2011 .

[34]  Ali Ahmadian,et al.  Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting , 2014 .

[35]  M A Matos,et al.  Setting the Operating Reserve Using Probabilistic Wind Power Forecasts , 2011, IEEE Transactions on Power Systems.

[36]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[37]  H Zareipour,et al.  Wind Power Prediction by a New Forecast Engine Composed of Modified Hybrid Neural Network and Enhanced Particle Swarm Optimization , 2011, IEEE Transactions on Sustainable Energy.

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

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

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

[41]  V M F Mendes,et al.  Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Wind Power Forecasting in Portugal , 2011, IEEE Transactions on Sustainable Energy.

[42]  Adrian E. Raftery,et al.  Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction , 2010 .

[43]  Hui Liu,et al.  Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks , 2013 .

[44]  C. Balakrishna Moorthy,et al.  APPLICATION OF GENETIC ALGORITHM TO NEURAL NETWORK MODEL FOR ESTIMATION OF WIND POWER POTENTIAL , 2010 .

[45]  Vladimiro Miranda,et al.  Time-adaptive quantile-copula for wind power probabilistic forecasting , 2012 .

[46]  Mohan Kolhe,et al.  Generalized feed-forward based method for wind energy prediction , 2013 .

[47]  Jianxue Wang,et al.  Review on probabilistic forecasting of wind power generation , 2014 .

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

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

[50]  Joao P. S. Catalao,et al.  Short-term wind power forecasting in Portugal by neural networks and wavelet transform , 2011 .

[51]  A. K. Choudhary,et al.  Short-term wind speed forecasting using feed-forward back-propagation neural network , 2011 .

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

[53]  John Bjørnar Bremnes,et al.  Probabilistic wind power forecasts using local quantile regression , 2004 .