Artificial neural networks in wave predictions at the west coast of Portugal

In coastal and open ocean human activities, there is an increasing demand for accurate estimates of future sea state. In these activities, predictions of wave heights and periods are of particular importance. In this study, two different neural network strategies were employed to forecast significant wave heights and zero-up-crossing wave periods 3, 6, 12 and 24 h in advance. In the first approach, eight simple separate neural nets were implemented to simulate every wave parameter over each prediction interval. In the second approach, only two networks provided simultaneous forecasts of these wave parameters for the four prediction intervals. Two independent sets of measurements from a directional wave buoy moored off the Portuguese west coast were used to train and to validate the artificial neural nets. Saliency analysis of the results permitted an optimization of the networks' architectures. The optimal learning algorithm for each case was also determined. The short-term forecasts of the wave parameters verified by actual observations demonstrate the suitability of the artificial neural technique

Using an Artificial Neural Network for Wave Height Forecasting in the Red Sea

184-191Artificial Neural Networks (ANNs) are widely used in the field of wave forecasting as data-based soft-computing techniques that do not require prior knowledge regarding the nature of the relationships between the forecasted waves and the controlling physical mechanisms. Among ANN-techniques is the Nonlinear Auto-Regressive Network with eXogenous inputs (NARX), based on which two models were developed in this study to predict the significant wave heights in Eastern Central Red Sea for the next 3, 6, 12 and 24 h. The two NARX-based models differ only by the inclusion of the variance between wind and wave directions in one model and not in the other. Both models have shown the ability to efficiently predict the significant wave heights up to 12 hours in advance. However, the outperformance of the model that included the difference between wind and wave directions indicated the significance of the inclusion of such an input term

Variabilidade da pluma estuarina do Tejo : impacto na circulação e hidrologia costeira

Mestrado em Meteorologia e Oceanografia FísicaAs descargas dos estuários formam uma pluma que é advetada para a região costeira adjacente, durante a maré vazante, modulando a circulação e hidrografia costeiras. Na costa central portuguesa, a pluma do Estuário do Tejo influencia a hidrografia da região, controlando a dinâmica local. Na atualidade, estudos de plumas estuarinas são realizados utilizando modelos de circulação e de transporte de alta-resolução e sofisticados métodos de análise de dados, como as redes neuronais. O objetivo deste estudo consiste em analisar os padrões da descarga estuarina do Tejo na zona costeira adjacente, sob diferentes condições de vento e eventos de descarga estuarinas elevadas. Neste âmbito, implementou-se o modelo 3D de circulação e transporte MOHID, utilizando um método de downscaling do modelo Operational Model for the Portuguese Coast (PCOMS) (6 km) para a ROFI do Estuário do Tejo (500m). De modo a melhorar os forçamentos atmosféricos do modelo costeiro, implementou-se uma nova aplicação de alta-resolução (2 km) do modelo WRF para a região. A validação dos modelos de circulação e de transporte foram realizadas para o período de Julho a Dezembro de 2012. Resultados numéricos demonstram uma reprodução correta dos padrões superficiais quando comparados com dados observados de velocidade da corrente, salinidade e temperatura da água. O erro médio quadrático (RMSE) das correntes de superfície mostrou um desvio médio inferior a 16 cms−1. Comparação da temperatura superficial da agua com os dados remotos do sensor MODIS-Aqua identificaram um desvio inferior a 2oC, demostrando a qualidade das previsões do modelo em reproduzir os padrões dinâmicos costeiros. De modo a estudar a variabilidade costeira causada pela descarga estuarina do Tejo, simularam-se 5 cenários utilizando ventos favoráveis de upwelling e downwelling, e descargas do Rio Tejo de 1500, 3000 e 5000 m3s−1. As simulações dos cenários foram realizadas para o período de 8-31 de Novembro de 2012, devido à ocorrência de padrões favoráveis de vento. Com o objetivo de quantificar a variabilidade costeira, realizou-se uma analise SOM com 1x4 padrões espaciais dos campos de salinidade à superfície. Os resultados demonstram que as descargas fluviais são o principal forçamento nas simulações, só superados pelo vento aquando se registam baixas descarga fluviais. Secções transversais da salinidade mostraram uma profundidade da pluma estuarina com cerca de 15 m na zona da embocadura do estuário, reduzindo-se para 10 m na zona costeira adjacente ao Cabo da Roca. A tensão do vento revelou ter um papel importante na dispersão da pluma, sendo responsável pelo transporte para norte ou para sudoeste da mesma. Em todos os cenários, os ventos favoráveis a downwelling transportam a pluma para norte encostada à costa, enquanto ventos de upwelling transportam a pluma para sudoeste. A nova implementação de alta resolução em 3D desenvolvida neste trabalho permite retirar informação extra acerca da dinâmica da pluma estuarina sob diferentes condições de vento e descarga do Rio Tejo. Padrões distintos de dispersão foram observados, permitindo uma melhoria do conhecimento da circulação e hidrografia da região. Para trabalhos futuros, este modelo pode permitir o acoplamento de modelos biogeoquímicos ou de derrame de hidrocarbonetos, temas importantes e desafiantes para a complexa zona costeira adjacente ao Estuário do Tejo.Buoyant discharges from estuaries form an outflow plume that is advected onto the near shelf during the ebb tide, modulating the circulation and hydrography of the adjacent coast. In the central coast of Portugal, the plume from the Tagus Estuary influences the hydrography of the area, controlling local dynamics. Nowadays, estuarine plume propagation studies are performed using high-resolution circulation and transport models and sophisticated data analyses tools, as the artificial neural networks. The main aim of this work consists in studying the Tagus Estuarine outflow behaviour under different wind forcing conditions and variable river discharge events. For this purpose, a 3D circulation and transport model (www.mohid.com) was implemented for the region, using a nested downscaling approach from the Operational Model for the Portuguese Coast (PCOMS) (6 km) to the Tagus ROFI (500 m). To improve the atmospheric circulation model forcing, a new high-resolution atmospheric model (2 km) was implemented for the region, using the WRF model. To validate the circulation and transport models, a simulation period between July and December 2012 was used. Numerical predictions shown an accurate reproduction of the surface patterns when compared with observed data of current velocity, salinity and water temperature. The root mean square error (RMSE) of surface currents revealed a mean deviation lower than 16 cm s−1. SST comparison with MODIS-Aqua remote sensing imagery shows a deviation lower than 2oC, revealing the model accuracy in reproducing coastal dynamics. In order to study coastal variability due to the Tagus estuarine outflow, five scenarios were simulated under upwelling and downwelling favourable winds and Tagus river discharge of 1500, 3000 and 5000 m3s−1. The scenarios simulations were performed for the days 8-31 of November 2012 because of favourable distinct wind patterns. In order to quantify the region variability 1×4 Self-Organizing Map (SOM) of the surface salinity fields were performed. The results show that river discharge is the main controlling forcing for the scenarios simulation, only overtaken by the wind, when low discharge values were present. The cross-shelf sections show a depth of the plume bulge of 15 m near the mouth of the estuary, reducing to 10 m in the far region, near Cabo da Roca. The wind stress played a powerful role in the dispersion of the bulge, being responsible for the north or southwest transport of the plume. In all scenarios, under downwelling favourable winds the plume is compressed toward the coast, and under upwelling winds the plume follows a SW direction, being advected offshore. The new high resolution 3D implementation developed in this study provides extra information about the Tejo estuarine plume dynamic under different conditions of winds and river discharge. Distinct spatial dispersion was observed improving the knowledge of the region circulation and hydrography. Thus, this validated implementation can be used for new studies, such as coupling of a biogeochemistry or oil spill models, topics that are important and challenging for a complex coastal region such as the off the Tagus Estuary

Hydrolink 2021/3. Offshore renewable energy

Topic: Offshore renewable energ

Ocean forecasting for wave energy production

There are a variety of requirements for future forecasts in relation to optimizing the production of wave energy. Daily forecasts are required to plan maintenance activities and allow power producers to accurately bid on wholesale energy markets, hourly forecasts are needed to warn of impending inclement conditions, possibly placing devices in survival mode, while wave-by-wave forecasts are required to optimize the real-time loading of the device so that maximum power is extracted from the waves over all sea conditions. In addition, related hindcasts over a long time scale may be performed to assess the power production capability of a specific wave site. This paper addresses the full spectrum of the aforementioned wave modeling activities, covering the variety of time scales and detailing modeling methods appropriate to the various time scales, and the causal inputs, where appropriate, which drive these models. Some models are based on a physical description of the system, including bathymetry, for example (e.g., in assessing power production capability), while others simply use measured data to form time series models (e.g., in wave-to-wave forecasting). The paper describes each of the wave forecasting problem domains, details appropriate model structures and how those models are parameterized, and also offers a number of case studies to illustrate each modeling methodology

A copula-based approach for the estimation of wave height records through spatial correlation

publisher: Elsevier articletitle: A copula-based approach for the estimation of wave height records through spatial correlation journaltitle: Coastal Engineering articlelink: http://dx.doi.org/10.1016/j.coastaleng.2016.06.008 content_type: article copyright: © 2016 The Authors. Published by Elsevier B.V

Investigation of a coastal wind farm at northeast Brazil using the WRF model

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2018.Dentre as fontes de energias renováveis, a eólica possui o maior crescimento no mundo, tornando-se uma importante fonte de energia mundial. Devido à sua natureza intermitente, os operadores do sistema elétrico normalmente utilizam modelos numéricos de previsão do tempo, e suas simulações do vento, para garantir o suprimento de energia e balanceamento de carga no sistema interligado nacional. Com o objetivo de contribuir para o crescimento da energia eólica no Brasil, este estudo avaliou e otimizou simulações do vento obtidas pelo modelo WRF na Usina Eólica de Pedra do Sal, assim como investigou previsões de energia eólica obtidas pela combinação do WRF e de redes neurais artificiais. A usina eólica de 18 MW está localizada na costa do Nordeste brasileiro, o que, devido a diferentes características na temperatura, rugosidade e superfície da terra/mar, introduz desafios adicionais na simulação do vento por modelos numéricos. Desta forma, o estudo englobou três resultados principais. Primeiro, uma análise de sensibilidade do modelo de camada limite planetária foi realizada nas simulações do WRF com um domínio, de 15 km de resolução de grade, para o mês de setembro de 2013. Os menores erros na simulação do vento foram obtidos utilizando a parametrização MYNN2 (RMSE de 2,12 m/s e Bias de -1,37 m/s). Segundo, os resultados do WRF foram interpolados em locais onshore e offshore, procedimento nomeado de abordagem de interpolação. Devido ao vento local ser influenciado pela proximidade do mar, os dados interpolados na localização offshore OFF-2 exibiram a melhor performance, resultando em RMSE de 1,69 m/s e Bias de -0,10 m/s. Isso representa uma redução de 20,2% do RMSE e 92,7% do Bias, quando comparado aos resultados obtidos no local usual de interpolação, a posição da torre anemométrica (ON-T). Terceiro, a abordagem de interpolação foi investigada na previsão de geração eólica com redes neurais, de setembro a dezembro de 2013. Dados das posições ON-T e OFF-2 serviram de entrada para duas redes neurais feedforward de três camadas. Para uma mesma arquitetura de 80 neurônios, as previsões de geração eólica de NN-OFF-2 resultaram em menores valores de RMSE e Bias, 7,7% e 7,4%, respectivamente, que as previsões de NN-ON-T. Em conclusão, a interpolação offshore dos resultados do WRF provou ser uma abordagem viável a ser implementada em previsões de vento e de geração eólica na Usina Eólica de Pedra do Sal, pois utiliza menor tempo de processamento, resulta em maior performance e menores valores de erros de previsão quando comparada a outras simulações.Among the renewable energy sources, wind energy has the fastest growth in the world and became an important source of energy worldwide. Due to its intermittent nature, energy system operators normally rely on numerical weather predictions, and their wind simulations, in order to ensure energy supply and load balancing in the system. Aiming to contribute to the wind energy growth in Brazil, this study evaluated and optimized wind simulations obtained by the WRF model in Pedra do Sal wind farm, as well as assessed wind power predictions obtained by the combination of WRF and artificial neural networks. The 18 MW wind farm is located on the northeast coast of Brazil, which, due to different thermal, roughness and surface features of land/sea, introduces additional challenges in the wind simulation by numerical models. The study covered three main results. First, a sensitivity analysis of the planetary boundary layer scheme was performed in one-domain WRF simulations, with 15 km of grid resolution, for September 2013. The lowest wind simulation errors were obtained using MYNN2 parameterization (RMSE of 2.12 m/s and Bias of -1.37 m/s). Second, the WRF results were interpolated in onshore and offshore locations, named as interpolation approach. Since the local wind is influenced by the proximity to the sea, the data interpolated at the offshore location OFF-2 displayed the best performance, showing a RMSE of 1.69 m/s and Bias of -0.10 m/s. This represents a reduction of 20.2% of the RMSE and 92.7% of the Bias when compared to results obtained at the usual interpolation location, the met mast position (ON-T). Third, the interpolation approach was investigated on the wind power prediction with neural networks, from September to December of 2013. ON-T and OFF-2 data were employed as input of two three-layers feedforward networks. For the same 80-neurons architecture, the wind power predictions of NN-OFF-2 showed lower RMSE and Bias, 7.7% and 7.4% respectively, than the NN-ON-T forecasts. In conclusion, the offshore interpolation of the WRF results proved to be a feasible approach to be implemented in wind speed and power predictions at the coastal Pedra do Sal wind farm, since it uses less computational time, achieves higher performance and lower prediction errors when compared to other simulations