Wind energy forecasting with neural networks: a literature review

Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of there newable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power fore castingis surging based on Machine Learning Deep Learning techniques. This paper analyses the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years.Peer ReviewedPostprint (published version

Closed G2 forms and special metrics

170 p.En esta tesis se aborda el estudio y construcción de variedades G2 calibradas. Una tal variedad es una variedad de Riemann, de dimensión 7, con una métrica de Riemann definida por una cierta 3-forma diferencial, denominada G2 forma, la cual no sólo es invariante por la acción del grupo excepcional G2 sino que también es cerrada y, por lo tanto, define una calibración en el sentido de Harvey y Lawson. Los dos primeros capítulos de esta memoria se dedican a la construcción de nuevos ejemplos de esas variedades, tanto en el caso compacto como no-compacto. En particular, mostramos que el mapping torus de un difeomorfismo de una variedad half-flat simpléctica, tal que la estructura half-flat es preservada por el difeomorfismo, es una variedad G2 calibrada. En los capítulos 3 y 4 estudiamos la existencia de métricas especiales (Einstein y Ricci solitones) determinadas por G2 formas cerradas. Por una parte, sabemos que el comportamiento del tensor de Ricci de la métrica inducida por una G2 forma está estrechamente relacionado con el comportamiento de la propia G2 forma. En particular, Cleyton e Ivanov probaron que ninguna variedad compacta, de dimensión 7, admite una estructura G2 calibrada tal que la métrica inducida sea Einstein, salvo que la G2 forma sea también cocerrada y, por lo tanto, el grupo de holonomía de la métrica es un subgrupo de G2. En el capítulo 3 exploramos la versión no compacta de este resultado, obteniendo un resultado equivalente para variedades (no compactas) resolubles.En el último capítulo, determinamos las nilvariedades compactas que poseen una G2 forma calibrada induciendo un nilsolitón. Para cada una de esas variedades, estudiamos el flujo Laplaciano, y mostramos los primeros ejemplos compactos tales que la solución del flujo Laplaciano está definida en un intervalo no acotado

“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

Compact solvmanifolds with calibrated and cocalibrated G2-structures

We give a method to obtain new solvable 7-dimensional Lie algebras endowed with closed and coclosed G2-structures starting from 6-dimensional solvable Lie algebras with symplectic half-flat and half-flat SU(3)-structures, respectively. Provided the existence of a lattice for the corresponding Lie groups we obtain new examples of compact solvmanifolds endowed with calibrated and cocalibrated G2-structures. As an application of this construction we also obtain a formal compact solvmanifold with first Betti number b1=1 endowed with a calibrated G2-structure and such that does not admit any invariant torsion-free G2-structure

Predicting wind energy generation with recurrent neural networks

Decarbonizing the energy supply requires extensive use of renewable generation. Their intermittent nature requires to obtain accurate forecasts of future generation, at short, mid and long term. Wind Energy generation prediction is based on the ability to forecast wind intensity. This problem has been approached using two families of methods one based on weather forecasting input (Numerical Weather Model Prediction) and the other based on past observations (time series forecasting). This work deals with the application of Deep Learning to wind time series. Wind Time series are non-linear and non-stationary, making their forecasting very challenging. Deep neural networks have shown their success recently for problems involving sequences with non-linear behavior. In this work, we perform experiments comparing the capability of different neural network architectures for multi-step forecasting in a 12 h ahead prediction. For the Time Series input we used the US National Renewable Energy Laboratory’s WIND Dataset [3], (the largest available wind and energy dataset with over 120,000 physical wind sites), this dataset is evenly spread across all the North America geography which has allowed us to obtain conclusions on the relationship between physical site complexity and forecast accuracy. In the preliminary results of this work it can be seen a relationship between the error (measured as R2R2 ) and the complexity of the terrain, and a better accuracy score by some Recurrent Neural Network Architectures.Peer ReviewedPostprint (author's final draft

Go with the flow: Recurrent networks for wind time series multi-step forecasting

One of the ways of reducing the effects of Climate Change is to rely on renewable energy sources. Their intermittent nature makes necessary to obtain a mid-long term accurate forecasting. Wind Energy prediction is based on the ability to forecast wind speed. This has been a problem approached using different methods based on the statistical properties of the wind time series. Wind Time series are non-linear and non-stationary, making their forecasting very challenging. Deep neural networks have shown their success recently for problems involving sequences with non-linear behavior. In this work, we perform experiments comparing the capability of different neural network architectures for multi-step forecasting obtaining a 12 hours ahead prediction using data from the National Renewable Energy Laboratory's WIND datasetPeer ReviewedPostprint (published version

Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level

Wind Energy generation depends on the existence of wind, a meteorological phenomena intermittent by nature, with the consequence of generating uncertainty on the availability of wind energy in the future. The grid stability processes require continuous forecasting of wind energy generated. Forecasting wind energy can be performed either by using weather forecast data or by projecting (or regressing) the past time-series data observations into the future. This last method is the statistical or time series approach. Wind Time Series show non-linearity and non-stationarity properties, and these two properties increase the complexity of the forecasting task using statistical methodologies. In this paper we explore the use of deep learning techniques, which can represent non-linearity, to the wind speed prediction using the largest public wind dataset available, the Wind Toolkit from the National Renewable Laboratory of the US. Several deep network architectures like Multi Layer Perceptrons, Convolutional Networks or Recurrent Networks have been tested on the 126,692 wind-sites and with the results obtained valuable comparisons and conclusions have been obtained. The distribution of the wind sites across the North American Geography has allowed to include in the analysis relationships between terrain, wind forecast complexity and deep methods. With the developed testing workbench and with the availability of the Barcelona Supercomputing Center new architectures are being developed. This work concludes with the feasibility of deep learning architectures for the wind and energy forecasting.We thank the Barcelona Supercomputing Center for the extensive use of their infrastructure in this project, and the United States National Renewable Energy Laboratory (NREL) for the use of their wind energy datasets. This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, and by the Generalitat de Catalunya (contracts 2014-SGR-1051)Peer ReviewedPostprint (published version

The value of minilaparotomy for total hysterectomy for benign uterine disease: A comparative study with conventional Pfannenstiel and laparoscopic approaches

<p>Abstract</p> <p>Background</p> <p>The aim of this paper is to review and compare the results obtained using the Pfannenstiel, laparoscopy and minilaparotomy approaches for total hysterectomy procedure in relation to benign uterine diseases.</p> <p>Methods</p> <p>A retrospective data analysis was performed on 165 patients who underwent hysterectomy for benign uterine diseases at our centre during the period 2004 to 2006.</p> <p>Findings</p> <p>The minilaparotomy procedure was the fastest procedure with a mean time of 73.4 minutes (range: 67.85 to 78.94 minutes, p < 0.001). Hospital stay was shortest for laparosopic procedure (mean time: 3.24 days, range: 2.86 to 3.61 days) (p < 0.001). The rate of intraoperative and postoperative complications were not statistical different among three procedures.</p> <p>Conclusion</p> <p>The minilaparotomy procedure offers a minimally invasive option for total hysterectomy due to benign uterine disease.</p

Two-dimensional power Doppler-three-dimensional ultrasound imaging of a cesarean section dehiscence with utero-peritoneal fistula: a case report

<p>Abstract</p> <p>Introduction</p> <p>An imaging diagnosis after an iterative cesarean delivery is reviewed demonstrating a fine ultrasound-pathologic correlation.</p> <p>Case presentation</p> <p>A 33-year-old woman (G3, P3) presented referring intense dysmenorrhea and intermenstrual spotting since her third cesarean delivery, 1 year before. A cesarean section dehiscence with utero-peritoneal fistula was diagnosed by transvaginal ultrasound.</p> <p>Conclusion</p> <p>We can conclude that transvaginal two-dimensional power Doppler and three-dimensional ultrasound are highly accurate in detecting cesarean section dehiscence and uterine fistula.</p

Prospective Development of the Tourism on line Distribution Channel

We know of the development of the online channel in the tourism distribution system, due to the intensive application of ICT. This seems announce like a revolution in the industry, with structural changes in production, and in the behaviour of the involved agents. However, more advances in the literature are needed in order to gain theories to the body of knowledge. With this aim, qualitative research is done through in-depth interviews driven to a selection of experts in the eld; managers of multi-channel distribution systems as well as of online channel in exclusive. Applying the triangulation of data methodology provides results which reveal certain types of changes, derived from the adoption of ICT, that tourism companies adopters consider as signi cant competitive advantages, with capability to affect companies’ results and increase its power within the distribution channel. This offers great potential for the future development of the online channel and for the companies that use it exclusively