Validation of copula-based weather generator for maintenance model of offshore wind farm

Skobiej, Bartosz; Niemi, Arto

doi:10.1007/s13437-021-00255-x

Cited by 6 publications

(3 citation statements)

References 29 publications

(19 reference statements)

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“…This Power-to-X project is currently proposed to be put into operation in 2029. Paper [87] presents an offshore wind weather conditions modeling, with a specific focus on maintenance aspects. Paper [88] presents a floating wind turbines' reliability approach, which is of direct relevance to their power production conditions.…”

Section: Power-to-x Technology References Of Relevance To Floating Wi...mentioning

confidence: 99%

A Review of Perspectives on Developing Floating Wind Farms

Maktabi,

Rusu

2024

Inventions

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Floating wind is becoming an essential part of renewable energy, and so highlighting perspectives of developing floating wind platforms is very important. In this paper, we focus on floating wind concepts and projects around the world, which will show the reader what is going on with the projects globally, and will also provide insight into the concepts and their corresponding related aspects. The main aim of this work is to classify floating wind concepts in terms of their number and manufacturing material, and to classify the floating wind projects in terms of their power capacity, their number, character (if they are installed or planned) and the corresponding continents and countries where they are based. We will classify the corresponding additional available data that corresponds to some of these projects, with reference to their costs, wind speeds, water depths, and distances to shore. In addition, the floating wind global situation and its corresponding aspects of relevance will be also covered in detail throughout the paper.

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Section: Power-to-x Technology References Of Relevance To Floating Wi...mentioning

confidence: 99%

A Review of Perspectives on Developing Floating Wind Farms

Maktabi,

Rusu

2024

Inventions

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“…The article in [17] discussed the characteristic of modeling weather conditions for marine weather forecasting by utilizing copula-based method. However, the prevailing prediction method of visibility is specifically based on prediction using numerical values moreover same as performing weather prediction.…”

Section: Related Workmentioning

confidence: 99%

Classification for Big Data Driven Marine Weather Forecasting Using Machine Learning Techniques

J¹,

Radha²

2023

IJSREM

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Marine weather forecasting has raised sizeable awareness in numerous ocean related domains. With immeasurable portion of data to handle with, big data problem solving unfolds gateways for abundance of predictions. Machine Learning (ML) is an essential algorithm for big data prediction. However with the increasing size and nature of data i.e., Big Data, Marine Weather Forecasting with Big Data with minimum time, error and maximum accuracy is of major concern to be addressed. In this work, a method called, Perceptred-based Feature and Kriging Gradient Boost Classification (PF-KGBC) is introduced with big data with the objective of improving the prediction performance marine weather with high accuracy and less time consumption. The PF-KGBC method is split into two parts. They are feature selection using perceptron classifier model and classification using Kriging Ensemble extreme Gradient Boost for marine weather forecasting. With the assistance of supervised learning algorithm based on perceptron classifier that involves a functional input represented by vector of numbers belongs to particular class. Based on linear predictor function set of weights are integrated with feature vector to make essential marine weather feature vector. After feature selection process, Kriging Ensemble extreme Gradient Boost Classification is performed with the purpose of forecasting marine weather data. Here, the weak learner is combined to form strong classifier. Kriging regression estimates the dependent data variation when any of factors or values in independent data gets changed. With this accurate marine weather prediction with high accuracy and lesser time consumption is ensured. Meanwhile, we analyzed the results of the proposed PF- KGBC method compared with other conventional methods, and the running performance on the Java platform. The results show that the proposed method achieved satisfactory prediction results and improvements were observed in terms of accuracy, time and error rate considerably. Keywords: Machine Learning, Perceptron, Feature Selection, Kriging, Gradient Boost, Classification, Ensemble

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“…Integrating the stochastic weather generator within the decision support tool for inputs will enhance the overall performance of the decision support tool. Several approaches have been suggested to develop the stochastic weather generator in the literature, such as Copula approach [5], Gaussian statistics, Auto Regressive Integrated Moving-Average (ARIMA) processes, Markov processes and Langevin process [6]. Although effective, these widely-used approaches are not able to capture the nonlinear and non-stationary characteristics that are shown in wind and wave met-ocean parameters.…”

Section: Introductionmentioning

confidence: 99%

Advancing weather predictions for offshore wind farm maintenance through deep learning

Dighe,

Liu

2024

J. Phys.: Conf. Ser.

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Historical met-ocean data are widely used in the decision support tools to evaluate different operations & maintenance (O&M) strategies for offshore wind energy. Although effective, they are often very limited, which may not be able to represent prolonged offshore weather conditions at the wind farm sites. This hinders their application to the O&M planning. In this paper, a deep learning approach is proposed to build up the stochastic weather generator, in which the long short-term memory neural network is leveraged to simulate wind and wave time series data. The neural network is trained using the (limited) historical met-ocean dataset to accurately capture the statistical characteristics of wind and wave conditions. The results demonstrate the effectiveness of the proposed stochastic weather generator in delivering both open-loop and closed-loop forecasting for wind speed and significant wave height data, thereby supporting both corrective and preventive maintenance activities. The case study reveals that the open-loop forecast excels in short-term hourly met-ocean parameter predictions, while the closed-loop forecast proficiently captures the met-ocean patterns within a predefined window. Although the closed-loop forecast for wave parameters generally follows the measurements trend, it diverges from the actual measurements; a discrepancy likely due to the complex spatial-temporal dynamics of waves not completely captured by the LSTM model. The proposed LSTM model, considered as a complementary but connected solution, is able to enhance the utility of the limited historical data in O&M planning.

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Validation of copula-based weather generator for maintenance model of offshore wind farm

Cited by 6 publications

References 29 publications

A Review of Perspectives on Developing Floating Wind Farms

A Review of Perspectives on Developing Floating Wind Farms

Classification for Big Data Driven Marine Weather Forecasting Using Machine Learning Techniques

Advancing weather predictions for offshore wind farm maintenance through deep learning

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