Comparison of Three Methods for Short-Term Wind Power Forecasting

Wind power forecasting is critical for effective grid operation and management. An accurate short-term wind forecasting model is an important tool for grid reliability and market-based ancillary services. However accurate prediction of wind power is not a trivial task. This is mainly because wind is stochastic in nature and a very local phenomenon, and therefore hard to predict. In this paper, we compared three methods for short-term wind power forecasting. Namely, a time series based method called Autoregressive Moving Average (ARMA), Artificial Neural Networks (ANNs), and a method based on hybridising Artificial Neural Networks (ANNs) and Fuzzy Logic called Adaptive Neuro-Fuzzy Inference Systems (ANFIS). It is shown that for a very short-term wind power forecasting, all the three methods perform similarly. However, for the short-term wind power forecasting, the ARIMA method performs better than both the ANNs and ANFIS. For longer time horizon (medium and long-term), the performance of ARMA deteriorated as compared to the other two methods.

[1]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[2]  Davide Astolfi,et al.  Wind Power Forecasting techniques in complex terrain: ANN vs. ANN-CFD hybrid approach , 2016 .

[3]  M. Negnevitsky,et al.  Short term wind power forecasting using hybrid intelligent systems , 2007, 2007 IEEE Power Engineering Society General Meeting.

[4]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[5]  Yongsheng Chen,et al.  Short-term wind speed and power forecasting using an ensemble of mixture density neural networks , 2016 .

[6]  S. Karsoliya,et al.  Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture , 2012 .

[7]  Elizabeta Lazarevska.  Comparison of different models for wind speed prediction , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[8]  Paras Mandal,et al.  A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

[9]  W. Rivera,et al.  Wind speed forecasting in the South Coast of Oaxaca, México , 2007 .

[10]  Henrik Madsen,et al.  Optimal combination of wind power forecasts , 2007 .

[11]  Komla A. Folly,et al.  Statistical Analysis of Wind Resources at Darling for Energy Production , 2012 .

[12]  Wen-Yeau Chang,et al.  A Literature Review of Wind Forecasting Methods , 2014 .

[13]  H. M. I. Pousinho,et al.  An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[14]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[15]  Paul Fleming,et al.  Using machine learning to predict wind turbine power output , 2013 .

[16]  Paras Mandal,et al.  An overview of forecasting problems and techniques in power systems , 2009, 2009 IEEE Power & Energy Society General Meeting.

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

[18]  M. Milligan,et al.  Statistical Wind Power Forecasting Models: Results for U.S. Wind Farms; Preprint , 2003 .

[19]  Wei-Jen Lee,et al.  Forecasting the Wind Generation Using a Two-Stage Network Based on Meteorological Information , 2009, IEEE Transactions on Energy Conversion.

[20]  Shuxiang Xu,et al.  A novel approach for determining the optimal number of hidden layer neurons for FNN’s and its application in data mining , 2008 .

[21]  Justin Heinermann,et al.  Wind Power Prediction with Machine Learning Ensembles , 2016 .

[22]  Rendani Mbuvha,et al.  Bayesian Neural Networks for Short Term Wind Power Forecasting , 2017 .

[23]  J. Cidrás,et al.  Review of power curve modelling for wind turbines , 2013 .

[24]  K. A. Folly,et al.  Wind power estimation using recurrent neural network technique , 2012, IEEE Power and Energy Society Conference and Exposition in Africa: Intelligent Grid Integration of Renewable Energy Resources (PowerAfrica).