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

Hamed H.H. Aly

Renewable Energy, 2020, vol. 147, issue P1, 1554-1564

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.

Keywords: Tidal currents forecasting; Smart grid; ANN; FSLSM; WNN; Clustering techniques (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148119314454
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:147:y:2020:i:p1:p:1554-1564

DOI: 10.1016/j.renene.2019.09.107

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().