Article

Short-Term Wave Forecasting for Real-Time Control of Wave Energy Converters

Dept. of Electron. Eng., Nat. Univ. of Ireland Maynooth, Maynooth, Ireland
IEEE Transactions on Sustainable Energy (Impact Factor: 3.66). 08/2010; 1(2):99 - 106. DOI: 10.1109/TSTE.2010.2047414
Source: IEEE Xplore

ABSTRACT

Real-time control of wave energy converters requires knowledge of future incident wave elevation in order to approach optimal efficiency of wave energy extraction. We present an approach where the wave elevation is treated as a time series and it is predicted only from its past history. A comparison of a range of forecasting methodologies on real wave observations from two different locations shows how the relatively simple linear autoregressive model, which implicitly models the cyclical behavior of waves, can offer very accurate predictions of swell waves for up to two wave periods into the future.

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    • "Moreover, taking into consideration the modern engineering-area of wave-energy conversion, a fresh and promising application of linear model is delineated. Actually, according with [31] "
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    ABSTRACT: This paper describes an expert system designed for the analysis of an incomplete, non-stationary and non-Gaussian, long-term, time series of wave significant heights by means of specific linear parametric model. Using this system makes it possible to complete missing-value gaps, forecast wave-height short-term evolution or simulate arbitrarily long sequences of wave data preserving the key statistical properties of the observed series, including autocorrelation, persistence over threshold, non-Gaussian distribution and seasonality. The implemented improvements bear on specific key tasks of ARMA setup procedure, i.e. preliminary analysis, parameter estimation and optimal model-configuration identification. Specifically, a Seasonal Trend decomposition based on Loess robust method is applied to compute more stable and detailed seasonal trend, allowing assuming more confidently its deterministic nature. Moreover, aiming at accurately estimating the model parameters, a proficient method is taken in, which is based on the robust Whittle’s approximation of the maximum log-likelihood function as well as on the direct-search, non-linear, multi-parameter, constrained, optimization technique called complex modified. Finally, an automatic expert system is developed, able to identify, almost correctly, ARMA orders by selecting the model with the smallest residuals variance and parameter numbers. Confident applicability of the suggested procedure is tested by means of both Monte Carlo simulations and comparisons of generated series with observed one, this latter measured offshore Alghero – Italy. Analysis of results clearly indicate that the accuracy in identifying the correct ARMA model is improved; furthermore, it is shown that the simulated time series exhibit all the primary statistical properties of the observed data, including winter and summer seasonal patterns as well as sea states sequencing, persistence and severeness.
    Full-text · Article · Sep 2015 · International Journal of Engineering and Technical Research
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    • "Studies of wave energy forecasting have generally involved the modelling of wave height and period using statistical and physics-based approaches. The statistical models used for this application include time-varying parameter regressions (see Reikard, 2009, 2013), unconditional kernel density estimation (see Ferreira and Guedes-Soares, 2002), neural networks (see, for example, Zamani et al., 2008), and autoregressive models (see, for example, Guedes-Soares and Ferreira, 1996; Guedes-Soares and Cunha, 2000; Fusco and Ringwood, 2010). Physics-based models are used by Hasselmann et al. (1976) (1980) (1985) and Janssen (1991, 2007). "
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    ABSTRACT: Wave energy has great potential as a renewable source of electricity. Installed capacity is increasing, and developments in technology mean that wave energy is likely to play an important role in the future mix of electricity generation. Short-term forecasts of wave energy are required for the efficient operation of wave farms and power grids, as well as for energy trading. The intermittent nature of wave energy motivates the use of probabilistic forecasting. In this paper, we evaluate the accuracy of probabilistic forecasts of wave energy flux from a variety of methods, including unconditional and conditional kernel density estimation, univariate and bivariate autoregressive moving average generalised autoregressive conditional heteroskedasticity (ARMA-GARCH) models, and a regression-based method. The bivariate ARMA-GARCH models are implemented with different pairs of variables, such as (1) wave height and wave period, and (2) wave energy flux and wind speed. Our empirical analysis uses hourly data from the FINO1 research platform in the North Sea to evaluate density and point forecasts, up to 24 h ahead, for the wave energy flux. The empirical study indicates that a bivariate ARMA-GARCH model for wave height and wave period led to the greatest accuracy overall for wave energy flux density forecasting, but its usefulness for point forecasting decreases as the lead time increases. The model also performed well for wave power data that had been generated from wave height and wave period observations using a conversion matrix.
    Full-text · Article · Jul 2015 · International Journal of Forecasting
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    • "Studies of wave energy forecasting have generally involved the modelling of wave height and period using statistical and physics-based approaches. The statistical models used for this application include time-varying parameter regressions (see Reikard, 2009, 2013), unconditional kernel density estimation (see Ferreira and Guedes-Soares, 2002), neural networks (see, for example, Zamani et al., 2008), and autoregressive models (see, for example, Guedes-Soares and Ferreira, 1996; Guedes-Soares and Cunha, 2000; Fusco and Ringwood, 2010). Physics-based models are used by Hasselmann et al. (1976) (1980) (1985) and Janssen (1991, 2007). "

    Full-text · Article · Jul 2015 · International Journal of Forecasting
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