Error correction of a predictive ocean wave model using local model approximation

Babovic, Vladan; Sannasiraj, S. A.; Chan, Eng Soon

doi:10.1016/j.jmarsys.2004.05.028

Cited by 43 publications

(24 citation statements)

References 15 publications

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“…The mean value µ WF (t) is, as proposed by [16], set equal to 0. The time-dependent uncertainty about weather forecast standard deviation σ WF (t) is chosen based on available forecast studies (wind: [17,18]; significant wave height [19][20][21]). Additionally an autocorrelation function is considered here in order to represent correlation between the successively following wind speeds and significant wave height values and preventing large variations, which is physically unrealistic, among values behind each other.…”

Section: Imperfect Weather Forecastmentioning

confidence: 99%

Sensitivity of Risk-Based Maintenance Planning of Offshore Wind Turbine Farms

Ambühl

Sørensen

2017

Energies

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Inspection and maintenance expenses cover a considerable part of the cost of energy from offshore wind turbines. Risk-based maintenance planning approaches are a powerful tool to optimize maintenance and inspection actions and decrease the total maintenance expenses. Risk-based planning is based on many input parameters, which are in reality often not completely known. This paper will assess the cost impact of this incomplete knowledge based on a case study following risk-based maintenance planning. The sensitivity study focuses on weather forecast uncertainties, incomplete knowledge about the needed repair time on the site as well as uncertainties about the operational range of the boat and helicopter used to access the broken wind turbine. The cost saving potential is estimated by running Crude Monte Carlo simulations. Furthermore, corrective and preventive (scheduled and condition-based) maintenance strategies are implemented. The considered case study focuses on a wind farm consisting of ten 6 MW turbines placed 30 km off the Danish North Sea coast. The results show that the weather forecast is the uncertainty source dominating the maintenance expenses increase when considering risk-based decision-making uncertainties. The overall maintenance expenses increased by 70% to 140% when considering uncertainties directly related with risk-based maintenance planning.

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Section: Imperfect Weather Forecastmentioning

confidence: 99%

Sensitivity of Risk-Based Maintenance Planning of Offshore Wind Turbine Farms

Ambühl

Sørensen

2017

Energies

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“…Therefore, in this paper, an alternate inverse approach based on genetic algorithm (GA) is employed, which has demonstrated significant improvements over the standard approach (see, e.g. References [18,19,[22][23][24]). …”

Section: Y Sun Et Almentioning

confidence: 99%

Applying local model approach for tidal prediction in a deterministic model

Sun

Sisomphon

Babovic

et al. 2008

Numerical Methods in Fluids

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SUMMARYIn recent years, a practice of tidal prediction based on a deterministic model or by a time series forecasting model has been established. A deterministic model can predict tidal movement and capture the dynamics of the flow pattern over the entire domain. However, due to the simplification of model settings and near shore effects, the accuracy of the numerical model can diminish. Time series forecasting is capable of capturing the underlying mechanism that may not be revealed in the deterministic model simulation. However, such data-driven forecast fails to maintain accuracy with the progress of forecast horizon. In this paper, a scheme that combines the advantages of these two methods is introduced. The model errors are forecasted to different time horizons using a data-driven approach, and are then superimposed on the simulation results in order to correct the model output. Based on the proposed method, it is found that the accuracy is significantly improved with more than 50% of the errors removed on the average.

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“…Consequently, the resulting overall model can be highly nonlinear since each of these linear approximations is made within separate neighbourhoods [15]. This forecasting procedure has been successfully applied directly to measured time series [11] as well as to time series of deterministic model errors [10] computed as residuals between simulation outputs and measurements at different sampling times. The second approach is desirable when an important contribution to describe the system dynamics can come from physically based numerical models.…”

Section: Error Correction Using Llmsmentioning

confidence: 99%

“…In these approaches, error correction at observation points can be undertaken by predicting errors using records of past residuals between model results and actual measurements. Error forecasting can be carried out using, for example, local linear models (LLMs) in real-time applications [10,11]. However, as it is not always sufficient to correct numerical models only at the points where observations are available, distribution of the predicted error from observation points over the remainder of the computational domain is often necessary.…”

Section: Introductionmentioning

confidence: 99%

Data assimilation of forecasted errors in hydrodynamic models using inter‐model correlations

Mancarella

Babovic

Keijzer³

et al. 2007

Numerical Methods in Fluids

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SUMMARYData-assimilation techniques of the Kalman filter type are considered to be the state-of-the-art approach for combining data information and deterministic numerical models with the objective of operational forecasting. This paper introduces, as an alternative, a faster and simpler data-assimilation technique that exploits inter-model correlations to distribute predicted errors. This scheme is performed in two steps: (i) prediction of the deterministic model errors at observation points using so-called local linear models and (ii) distribution of the forecasted errors over the computational domain employing a scheme based on deterministic inter-model correlations which describe the spatial nature of error structure. The method's advantage is that systematic error can be predicted by the error correction scheme, while the dynamics remain described by the deterministic model, which also establishes a basis for the spatial error distribution scheme. This relatively simple approach is inspired by original Kalman filter techniques but distinguishes error prediction and distribution in two different stages, hence allowing for data-driven error forecasting and off-line correction. In order to test the scheme's performance, a deterministic model of an artificial bay was constructed and run. The system was driven by specific forcing conditions and characterized by physical parameters that, in subsequent simulations, were deliberately manipulated to introduce errors into the model and test the scheme's capability.

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Error correction of a predictive ocean wave model using local model approximation

Cited by 43 publications

References 15 publications

Sensitivity of Risk-Based Maintenance Planning of Offshore Wind Turbine Farms

Sensitivity of Risk-Based Maintenance Planning of Offshore Wind Turbine Farms

Applying local model approach for tidal prediction in a deterministic model

Data assimilation of forecasted errors in hydrodynamic models using inter‐model correlations

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