A Proposed Algorithms for Tidal in-Stream Speed Model

Abstract

Abstract:In this paper we propose four models for tidal current speed and direction magnitude forecasting model. The first model is a Fourier series model based on the least squares method (FLSM), the second model is an artificial neural network (ANN), the third model is a hybrid of FLSM and ANN and the fourth model is a hybrid of ANN and FLSM for monthly forecasting of tidal current speed. These proposed models are ranked in order depending on their performance. These models are validated by using another set of data (tidal current direction). The proposed hybrid model of FLSM and ANN is highly accurate and outperforms. This study was done using data collected from the Bay of Fundy in 2008.

A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration

“…The Tidal Current Turbine Model Driven by DDPMSG [3,[29][30][31][32] The tidal current power (P t ) is represented by…”

“…Hybrid models offer the potential to achieve more accurate and robust forecasts by mitigating the limitations of individual methods and capturing complementary information from different sources. These hybrid methods have more advantages compared to others as they have the features of two or more different techniques in one model, but they require a longer time for training and convergence [1][2][3][4][5]. Several simple harmonic constitutions were used in the literature based on simplified tidal currents for tidal current forecasting [6].…”