An intelligent hybrid model of neuro Wavelet, time series and Recurrent Kalman Filter for wind speed forecasting

Abstract Wind speed Forecasting is the first step to integrate wind power into the main grid. It is important to improve the accuracy of wind speed forecasting to improve the load management side and the renewable energy integration. Due to the chaotic in the wind speed fluctuation the wind speed data forecasting is difficult. Many models are proposed in the literature for wind speed forecasting. This paper is proposing accurate hybrid models for wind speed forecasting to improve the overall system accuracy. These hybrid models involve various combinations of Wavelet and Artificial Neural Network (WNN and ANN), Time Series (TS) and Recurrent Kalman Filter (RKF). Three main hybrid models are proposed and tested. From those three models the best model with the highest performance is the hybrid of WNN, RKF, TS. The order of the techniques used in the hybrid models is very important. Different combinations with different orders are tested in this stage. Different models are tested with different techniques order. The proposed work is validated by using different unseen dataset with the proposed models and prove their effectiveness. All proposed models are accurate, but the best model is a hybrid of WNN, TS and RKF in sequence.

[1]  P. Drobinski,et al.  Sub-hourly forecasting of wind speed and wind energy , 2020 .

[2]  Hugh Rudnick,et al.  An Efficient Forecasting-Optimization Scheme for the Intraday Unit Commitment Process Under Significant Wind and Solar Power , 2018, IEEE Transactions on Sustainable Energy.

[3]  Heng Huang,et al.  Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities , 2015, IEEE Transactions on Smart Grid.

[4]  Marc A. Rosen,et al.  A heuristic-based approach for optimizing a small independent solar and wind hybrid power scheme incorporating load forecasting , 2019 .

[5]  Shiwen Mao,et al.  Adaptive Learning Hybrid Model for Solar Intensity Forecasting , 2018, IEEE Transactions on Industrial Informatics.

[6]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[7]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[8]  Jianzhou Wang,et al.  Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China , 2019, Journal of Cleaner Production.

[9]  Hamed H. H. Aly,et al.  A Proposed ANN and FLSM Hybrid Model for Tidal Current Magnitude and Direction Forecasting , 2014, IEEE Journal of Oceanic Engineering.

[10]  Wei Sun,et al.  A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network , 2020 .

[11]  Lior Rokach,et al.  A survey of Clustering Algorithms , 2010, Data Mining and Knowledge Discovery Handbook.

[12]  Zarita Zainuddin,et al.  Improved Wavelet Neural Networks for Early Cancer Diagnosis Using Clustering Algorithms , 2009 .

[13]  Jing Shi,et al.  Evaluation of hybrid forecasting approaches for wind speed and power generation time series , 2012 .

[14]  T. Funabashi,et al.  One-Hour-Ahead Load Forecasting Using Neural Networks , 2002 .

[15]  Lei Wu,et al.  Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method , 2016 .

[16]  M. E. El-Hawary,et al.  A proposed algorithms for tidal in-stream speed model , 2013 .

[17]  M. E. El-Hawary,et al.  The current status of wind and tidal in-stream electric energy resources , 2013 .

[18]  Yongqian Liu,et al.  Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model , 2019, Applied Energy.

[19]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[20]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[21]  Hamed H. H. Aly,et al.  A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid , 2020 .

[22]  Sujit Kumar Dash,et al.  Short Term Wind Power Forecasting using Hybrid Variational Mode Decomposition and Multi-Kernel Regularized Pseudo Inverse Neural Network , 2018 .