A novel approach for harmonic tidal currents constitutions forecasting using hybrid intelligent models based on clustering methodologies

Abstract Forecasting of renewable energy resources and their output power is playing a key role to improve the grid energy efficiency by making some load generation management. Tidal currents output power is depending on the tidal currents constitutions (speed magnitude and direction) forecasting. The accuracy of the tidal currents forecasting models is very important especially when we deal with smart grid and renewable energy integration. Many models are proposed in the literature for tidal currents forecasting but most of the models are not able to control the requirements of the smart grid due to their accuracy. This paper is proposing hybrid approaches for harmonic tidal currents constitutions forecasting based on clustering approaches to improve the system accuracy. These hybrid models involve various combinations of Wavelet and Artificial Neural Network (WNN and ANN) and Fourier Series Based on Least Square Method (FSLSM) techniques. The proposed work is validated by using two different datasets; one for tidal currents speed magnitude and the other one for tidal currents direction as well as K-fold cross validation. Simulations results prove the importance of the proposed models to improve the system performance. The proposed models are tested based on actual tidal currents data collected from the Bay of Fundy, Canada in 2008.

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

[2]  Wencong Su,et al.  A Combined Prognostic Model Based on Machine Learning for Tidal Current Prediction , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[3]  George Howard Darwin.  I. On an apparatus for facilitating the reduction of tidal observations , 1893, Proceedings of the Royal Society of London.

[4]  Hamed Aly.  FORECASTING, MODELING, AND CONTROL OF TIDAL CURRENTS ELECTRICAL ENERGY SYSTEMS , 2012 .

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

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

[7]  A. T. Doodson.  The Harmonic Development of the Tide-Generating Potential , 1921 .

[8]  Dong-Sheng Jeng,et al.  Application of artificial neural networks in tide-forecasting , 2002 .

[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]  Bang-Fuh Chen,et al.  Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea , 2007 .

[11]  Yue Jie,et al.  Correlative analysis of measured data between anemometer tower and WTG , 2012, 2012 8th International Conference on Computing and Networking Technology (INC, ICCIS and ICMIC).

[12]  V.Chandy John.  Harmonic tidal current constituents of the western Arabian Gulf from moored current measurements , 1992 .

[13]  Karl Aberer,et al.  Cluster-based aggregate forecasting for residential electricity demand using smart meter data , 2015, 2015 IEEE International Conference on Big Data (Big Data).

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

[15]  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.

[16]  J. Adamowski.  River flow forecasting using wavelet and cross‐wavelet transform models , 2008 .

[17]  Ritu Vijay,et al.  Tidal Data Analysis using ANN , 2008 .

[18]  Tsong-Lin Lee.  Back-propagation neural network for long-term tidal predictions , 2004 .