Abstract
In the estimation of future investments in the offshore wind industry, the operation and maintenance (O&M) phase plays an important role. In the simulation of the O&M figures, the weather conditions should contain information about the waves’ main characteristics and the wind speed. As these parameters are correlated, they were simulated by using a multivariate approach, and thus by generating vectors of measurements. Four different stochastic weather time series generators were investigated: Markov chains (MC) of first and second order, vector autoregressive (VAR) models, and long short-term memory (LSTM) neural networks. The models were trained on a 40-year data set with 1 h resolution. Thereafter, the models simulated 25-year time series, which were analysed based on several time series metrics and criteria. The MC (especially the one of second order) and the VAR model were shown to be the ones capturing the characteristics of the original time series the best. The novelty of this paper lies in the application of LSTM models and multivariate higher-order MCs to generate offshore weather time series, and to compare their simulations to the ones of VAR models. Final recommendations for improving these models are provided as conclusion of this paper.
Funder
Bundesministerium für Wirtschaft Und Klimaschutz
Reference34 articles.
1. Directive (EU) 2018/2001 of the European Parliament and of the Council on the Promotion of the Use of Energy from Renewable Sources,2018
2. Europa in Zahlen
https://www.wind-energie.de/themen/zahlen-und-fakten/europa/#:~:text=Windenergie%20in%20Europa&text=Mit%20458%20TWh%20produziertem%20Strom,und%20Gro%C3%9Fbritannien%20mit%2024%20GW
3. Levelised cost of energy, A challenge for offshore wind
4. Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review
5. The parameterisation and prediction of wave height and wind speed persistence statistics for oil industry operational planning purposes
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献