Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs) and distribution system operators (DSOs) in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT), genetic algorithm (GA) and artificial neural network (ANN) is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP) meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day’s wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method.

[1]  G.N. Kariniotakis,et al.  Probabilistic Short-term Wind Power Forecasting for the Optimal Management of Wind Generation , 2007, 2007 IEEE Lausanne Power Tech.

[2]  Han Li,et al.  A double-stage hierarchical hybrid PSO-ANN model for short-term wind power prediction , 2017, 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA).

[3]  Han Li,et al.  Short-term wind power forecasting using a double-stage hierarchical ANFIS approach for energy management in microgrids , 2017 .

[4]  J.C. Palomares-Salas,et al.  ARIMA vs. Neural networks for wind speed forecasting , 2009, 2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.

[5]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[6]  Ioannis B. Theocharis,et al.  A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation , 2007, Neurocomputing.

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  Les E. Atlas,et al.  Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.

[9]  Lars Landberg,et al.  Short-term prediction of the power production from wind farms , 1999 .

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

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

[12]  Jianhua Zhang,et al.  Short-term wind power forecasting using a double-stage hierarchical hybrid GA-ANN approach , 2017, 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(.

[13]  Dereje Shiferaw,et al.  Short-Term Wind Power Forecasting Using Artificial Neural Networks for Resource Scheduling in Microgrids , 2016 .

[14]  Joao P. S. Catalao,et al.  Neuro-Fuzzy Approach to Forecast Wind Power in Portugal , 2010 .

[15]  Singiresu S. Rao.  Engineering Optimization : Theory and Practice , 2010 .

[16]  Shuhui Li,et al.  Using neural networks to estimate wind turbine power generation , 2001 .

[17]  R. Jursa,et al.  Wind power prediction with different artificial intelligence models , 2008 .

[18]  J. Kleissl,et al.  Chapter 8 – Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation , 2013 .

[19]  Georges Kariniotakis,et al.  Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. , 2005 .

[20]  Ismael Sánchez,et al.  Short-term prediction of wind energy production , 2006 .

[21]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[22]  Yassine Charabi,et al.  Arabian summer monsoon variability: Teleconexion to ENSO and IOD , 2009 .

[23]  Henrik Madsen,et al.  A review on the young history of the wind power short-term prediction , 2008 .

[24]  Fulei Chu,et al.  VIBRATION SIGNAL ANALYSIS AND FEATURE EXTRACTION BASED ON REASSIGNED WAVELET SCALOGRAM , 2002 .

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

[26]  Alfred Baghramian,et al.  Short-Term and Very Short-Term Wind Power Forecasting Using a Hybrid ICA-NN Method , 2014 .

[27]  P. Tse,et al.  A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing , 2005 .

[28]  Deng Ying-ling,et al.  Short-Term Wind Speed Forecasting of Wind Farm Based on Least Square-Support Vector Machine , 2008 .

[29]  P. Tse,et al.  An improved Hilbert–Huang transform and its application in vibration signal analysis , 2005 .

[30]  J.B. Theocharis,et al.  A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation , 2004, IEEE Transactions on Energy Conversion.

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

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

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