Short and long-term wind turbine power output prediction

In the wind energy industry, it is of great importance to develop models that accurately forecast the power output of a wind turbine, as such predictions are used for wind farm location assessment or power pricing and bidding, monitoring, and preventive maintenance. As a first step, and following the guidelines of the existing literature, we use the supervisory control and data acquisition (SCADA) data to model the wind turbine power curve (WTPC). We explore various parametric and non-parametric approaches for the modeling of the WTPC, such as parametric logistic functions, and non-parametric piecewise linear, polynomial, or cubic spline interpolation functions. We demonstrate that all aforementioned classes of models are rich enough (with respect to their relative complexity) to accurately model the WTPC, as their mean squared error (MSE) is close to the MSE lower bound calculated from the historical data. We further enhance the accuracy of our proposed model, by incorporating additional environmental factors that affect the power output, such as the ambient temperature, and the wind direction. However, all aforementioned models, when it comes to forecasting, seem to have an intrinsic limitation, due to their inability to capture the inherent auto-correlation of the data. To avoid this conundrum, we show that adding a properly scaled ARMA modeling layer increases short-term prediction performance, while keeping the long-term prediction capability of the model

“Dust in the wind...”, deep learning application to wind energy time series forecasting

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.Peer ReviewedPostprint (published version

Wind Energy Forecast in Complex Sites with a Hybrid Neural Network and CFD based Method

Abstract The wind is an intermittent renewable energy source and the energy production forecast is a fundamental activity for many reasons (grid regulation, maintenance, etc.). In this work a hybrid method (based on weather forecast data, neural networks and computational fluid dynamics) and a pure neural network approach are compared in a complex terrain site. The post processing of real production data has been discovered to be a key activity. Treatment and filtering of data spreading out from the supervisory control and data acquisition system are fundamental both for training and testing methods reliability

Wind energy potential estimation using neural network and SVR approaches

The distribution of wind speed and the optimal assessment of wind energy potential are very important factors when selecting a suitable site for a wind power plant. In wind farm design projects for the supply of electrical energy, designers use the Weibull distribution law to analyse the characteristics and variations of wind speed in order to evaluate the wind potential. In our study we used two approaches, namely, the Multilayer Perceptron (MLP) approach and the Support Vector Machine (SVR) approach to determine a distribution law of wind speeds and to optimally evaluate the wind potential. These two approaches were compared to two well-known numerical methods which are the Justus Empirical Method (EMJ) and the Maximum Likelihood Method (MLM). The results show that the neural network approach produces a better fit of the distribution curve with an Root Mean Square Error (RMSE) of 0.00005016 at Lomé, 0.000040289 at Cotonou site and a more interesting estimate of the wind potential. After that SVR show a better result too with an RMSE of 0.0095618 at the Lomé site and 0.0053549 at the Cotonou site

Wind generation forecasting methods and proliferation of artificial neural network:A review of five years research trend

To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method

Vertical wind profile characterization and identification of patterns based on a shape clustering algorithm

Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. The present paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on z-normalization, shape-based distance metric solution and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using a commercialWindcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.The authors are grateful for the financial support from the Spanish Ministry of the Economy and Competitiveness and the European Union —ENE2016-78214-C2-2-R—and the Spanish Education, Culture and Sport Ministry —FPU16/042

Avaliação de aplicação de redes neurais artificiais em métodos de medição-correlação-predição

In this study a single artificial neural network (ANN) model was developed to predict the short term mean hourly wind speed and wind direction at target sites using short term mean hourly reference wind data. Standard multi-layered, feed-forward, backpropagation neural networks with single hidden layer architecture was designed using neural network toolbox for MATLAB. The hidden layers and output layer of the network consist of tangent sigmoid transfer function (tansig) and linear transfer function (purelin) as an activation function. Five different sites from Japan, Saudi Arabia, Jordan, France and Russia with different terrain complexity, completely different weather conditions, and different correlation coefficient between reference and target sites were tested. Single model was constructed, and two different approaches were experimented. Approach 1 made use of entire concurrent period dataset, the output values from the model was compared against the three methods: regression, matrix and neural network. Second approach was built on certain period of data and tested on unused data. The purpose behind the fabrication of this approach is to try and understand the neural network model. The results of approach 1 was that the neural network model is able to statistically perform better than other methods and equally well in predicting wind direction sectors. The maximum mean absolute percentage error for NN MATLAB model was found to be 62.5% in Japan to 23.7% in France. The model suffers in predicting the lower wind speeds which explains the distortion in wind frequency distribution and resulting in Power density deviation. The maximum deviation was -18.1% in Jordan and -7.9% in France. The sites in Japan, Saudi Arabia, France and Russia were considered for approach 2. The results were interesting, in case of japan the first month was better than the last month result. Overall the performance of the model was better in case of France followed by Russia site. The maximum deviation of Power density was noticed in case of Japan’s last month scenario -26.6% to minimum of about 3.2% in France and -5.2% was observed in case of Russia. In Saudi Arabia site, the only case where the concurrent period extends over a period of one year, the performance of the model was statistically good but suffers from same problem of previous cases. The deviation in power density was spotted around -21.4%.Neste estudo, foi desenvolvido um modelo de rede neural artificial (RNA) para prever a velocidade média do vento de curto prazo e a direção do vento em locais-alvo, usando dados de vento de referência de curto prazo. Foram projetadas redes neurais padrão multi-camadas, feed-forward, de propagação reversa com arquitetura de camada oculta única usando a caixa de ferramentas de rede neural para o MATLAB. As camadas ocultas e a camada de saída da rede consistem na função de transferência sigmóide tangente (tansig) e na função de transferência linear (purelin) como uma função de ativação. Foram testados cinco locais diferentes, Japão, Arábia Saudita, Jordânia, França e Rússia, com diferentes complexidades de terreno, condições climáticas completamente diferentes e diferentes coeficientes de correlação entre os locais de referência e os de destino. Foram testadas duas abordagens diferentes com o modelo construído. Na abordagem foi usado todo o conjunto de dados do período concorrente e os valores de saída do modelo foram comparados com três métodos em estudo: regressão, matriz e rede neural. A segunda abordagem foi construída usando apenas um determinado período de dados e o modelo foi testado em dados não utilizados. O objetivo desta segunda abordagem foi tentar entender o modelo de rede neural. Os resultados obtidos com a abordagem 1 aplicada aos 5 sítios em estudo permitiram verificar que o modelo de rede neural desenvolvido se apresenta estatisticamente melhor do que os outros métodos testados. Verifica-se que é capaz de prever bem a direção do vento por setores. Foi obtido um erro percentual médio absoluto máximo com o modelo NN MATLAB de 62,5% no Japão e de 23,7% na França. O modelo desenvolvido apresenta uma limitação na previsão das velocidades de vento mais baixas, o que explica a distorção na distribuição da frequência do vento e resulta no desvio da densidade de potência. O desvio máximo obtido para a densidade de potência foi de -18,1% na Jordânia e de -7,9% na França. Na abordagem 2 foram utilizados os dados do Japão, Arábia Saudita, França e Rússia. Os resultados foram interessantes. Verificou-se que no caso do Japão foi possível obter melhores resultados para o primeiro mês do que para o último mês. No geral, o desempenho do modelo foi melhor no caso da França, seguido pela Rússia. O desvio máximo da densidade de potência foi observado no caso do cenário do último mês do Japão -26,6% e foram observados desvios mínimos de cerca de 3,2% na França e -5,2% na Rússia. No site da Arábia Saudita, o único caso em que o período concorrente se estende por um período de um ano, o desempenho do modelo foi estatisticamente bom, verificando-se a mesma dificuldade de previsão de velocidades baixas. O desvio na densidade de potência foi de cerca de -21,4%

Impactos del Cambio Climático en la Generación de Energía Renovable y Evaluación de Escenarios de Generación Energética

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, leída el 26-04-2022This Thesis was titled Climate Change Impacts on Renewable Energy Generation and Energy Generation Scenarios.Climate change is attributed, among other factors, to greenhouse gas emissions produced by the energy sector (including the transport). At the same time, climate change is expected to affect this sector by changing the availability of resources, altering its enabling conditions and transforming demand patterns. This thesis addresses climate change impacts on renewable generation and electricity demand by providing an overview of the most relevant transformations projected in literature and by developing methodologies and quantitative analysis to ascertain the specific infuence in three case studies.The first and second chapters are focus on estimating climate change impacts in wind and photovoltaic generation in specific plants. Both provide physical and economic projections of expected changes, along with conclusions for the development of energy policies. The last chapter delves into how climate change and the scenarios proposed to curb it, can affect the demand for electricity in a region, due to the expected changes in the generation infrastructure and changes on the demand side such as a high penetration of electric vehicles...Esta Tesis se tituló Impactos del Cambio Climático en la Generación de Energía Renovable y Escenarios de Generación de Energía. El cambio climático se atribuye, entre otras variables, a las emisiones de gases de efecto invernadero producidas por el sector energético (incluyendo el transporte). Al mismo tiempo, el cambio climático se espera que pueda afectar a este sector cambiando la disponibilidad de sus recursos, alterando sus condiciones habilitantes y transformando los patrones de la demanda. Esta Tesis aborda los impactos del cambio climático en la generación renovable y cambios en el comportamiento de la demanda de electricidad, proporcionando una introducción a las transformaciones más relevantes proyectadas por la literatura y desarrollando metodologías y análisis cuantitativos que determinan el impacto específico en tres casos de estudio. El primer y el segundo capítulo se centran en determinar los cambios esperados en la generación eólica y fotovoltaica en plantas específicas, con especial atención en el calentamiento global. Ambos proporcionan proyecciones físicas y económicas de los cambios esperados, junto con conclusiones para el desarrollo de políticas energéticas. El último capítulo profundiza en cómo el cambio climático y los escenarios propuestos para frenarlo, pueden afectar a la demanda de electricidad de una región, debido a los cambios esperados en las infraestructuras de generación y en cambios por el lado de la demanda como sería una elevada penetración de los vehículos eléctricos...Fac. de Ciencias Económicas y EmpresarialesTRUEunpu