Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data

The aim of this work is to develop a prediction method for renewable energy sources in order to achieve an intelligent management of a microgrid system and to promote the utilization of renewable energy in grid connected and isolated power systems. The proposed method is based on the multi-resolution analysis of the time-series by means of Wavelet decomposition and artificial neural networks. The analysis of predictability of each component of the input data using the Hurst coefficient is also proposed. In this context, using the information of predictability, it is possible to eliminate some components, having low predictability potential, without a negative effect on the accuracy of the prediction and reducing the computational complexity of the algorithm. In the evaluated case, it was possible to reduce the resources needed to implement the algorithm of about 29% by eliminating the two (of seven) components with lower Hurst coefficient. This complexity reduction has not impacted the performance of the prediction algorithm.

[1]  Saif Ahmad,et al.  A temporal pattern identification and summarization method for complex time serial data , 2007 .

[2]  J. E. T. Segovia,et al.  Some comments on Hurst exponent and the long memory processes on capital markets , 2008 .

[3]  K. Agbossou,et al.  Development of a FPGA Based Real-Time Power Analysis and Control for Distributed Generation Interface , 2012, IEEE Transactions on Power Systems.

[4]  Johan Driesen,et al.  Balancing management mechanisms for intermittent power sources — A case study for wind power in Belgium , 2009, 2009 6th International Conference on the European Energy Market.

[5]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[6]  Urbano Nunes,et al.  Efficient feature selection for sleep staging based on maximal overlap discrete wavelet transform and SVM , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  S. N. Singh,et al.  AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network , 2012, IEEE Transactions on Sustainable Energy.

[8]  Kodjo Agbossou,et al.  Load Sharing Strategy for Autonomous AC Microgrids Based on FPGA Implementation of ADALINE&FLL , 2014, IEEE Transactions on Energy Conversion.

[9]  Indranil Bandyo.  Control and management of renewable generation — Necessity of look ahead study , 2013, 2013 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia).

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

[11]  Thierry Blu,et al.  Wavelet theory demystified , 2003, IEEE Trans. Signal Process..

[12]  G. Oh,et al.  Hurst exponent and prediction based on weak-form efficient market hypothesis of stock markets , 2007, 0712.1624.

[13]  K. Agbossou,et al.  Nonlinear model identification of wind turbine with a neural network , 2004, IEEE Transactions on Energy Conversion.

[14]  M. Shcherbakov,et al.  A Survey of Forecast Error Measures , 2013 .

[15]  Xiaofeng Meng,et al.  Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction , 2014, IEEE Transactions on Power Systems.

[16]  Eric Goutard.  Renewable energy resources in energy management systems , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[17]  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.

[18]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Jianzhou Wang,et al.  A hybrid forecasting approach applied to wind speed time series , 2013 .

[20]  Ruddy Blonbou,et al.  Very short-term wind power forecasting with neural networks and adaptive Bayesian learning , 2011 .

[21]  Ergin Erdem,et al.  ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .

[22]  Milde M. S. Lira,et al.  Application of wavelet and neural network models for wind speed and power generation forecasting in a Brazilian experimental wind park , 2009, 2009 International Joint Conference on Neural Networks.

[23]  Leon Freris,et al.  Renewable energy in power systems , 2008 .

[24]  Amit Jain,et al.  Wind speed forecasting: Present status , 2010, 2010 International Conference on Power System Technology.

[25]  Yongqian Liu,et al.  Hybrid Forecasting Model for Very-Short Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Features , 2014, IEEE Transactions on Smart Grid.

[26]  Shan Gao,et al.  Wind speed forecast for wind farms based on ARMA-ARCH model , 2009, 2009 International Conference on Sustainable Power Generation and Supply.

[27]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[28]  Zhang Yan,et al.  A review on the forecasting of wind speed and generated power , 2009 .

[29]  I. Rojas,et al.  Recursive prediction for long term time series forecasting using advanced models , 2007, Neurocomputing.

[30]  Minglei Duan,et al.  TIME SERIES PREDICTABILITY , 2002 .

[31]  Antti Sorjamaa,et al.  Multiple-output modeling for multi-step-ahead time series forecasting , 2010, Neurocomputing.

[32]  E Pelikán,et al.  Wind power forecasting by an empirical model using NWP outputs , 2010, 2010 9th International Conference on Environment and Electrical Engineering.

[33]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .