3,577 research outputs found
Situational-Context: A Unified View of Everything Involved at a Particular Situation
As the interest in the Web of Things increases, specially for the general population, the barriers to entry for the use of these technologies should decrease. Current applications can be developed to adapt their behaviour to predefined conditions and users preferences, facilitating their use. In the future,Web of Things software should be able to automatically adjust its behaviour to non-predefined preferences or context of its users. In this vision paper we define the Situational-Context as the combination of the virtual profiles of the entities (things or people) that concur at a particular place and time. The computation of the Situational-Context allow us to predict the expected system behaviour and the required interaction between devices to meet the entities’ goals, achieving a better adjustment of the system to variable contexts.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
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
Model and management indicators in industrial omnichannel (B2B)
The COVID-19 pandemic has driven increases in the provision of services through digital channels, even by more traditional companies. An Omnichannel model of service provision poses new management challenges for companies. This research reviews the literature on Omnichannel Management by companies whose clients are other companies (B2B) and classifies the different areas of research to date. The principal finding is that, despite considerable academic interest in Omnichannel management, there have been few studies of Omnichannel in the B2B field. This emphasizes a significant research gap to address. We have also outlined the Research Agenda to highlight future lines of research
“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
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
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
GeografĂa de las Plantas en La Alcarria Occidental y Mesa de Ocaña (I). Análisis florĂstico en cinco localidades representativas
It carries out a systematic and exhaustive floristic inventory in order to know the composition of vascular plants of the natural region of Western La Alcarria and “Mesa” of Ocaña (Central Iberian Peninsula). The survey method was applied in the NNE-SSW transect forming for five 1 Ă— 1-km U.T.M. grid squares (ED50) during one agricultural year (2002-2003). It allows consider almost the total richness notion of the floristic data and makes possible a geographical comparison of biodiversity in absolute terms. The Jaccard’s index is used to determine the floristic similarity between all grid cells and the taxonomic spectra is analyzed. The results show high levels of richness at all spatial levels (1634 native species at regional level; 690, in all five grid cells, and an average higher than 400 by grid cell). From highest to lowest, the regional typical spectrum presents a dominance of the daisy family, followed by grasses, legume family, crucifers and pink family; although the cells spectra have slight variations to this model.Se efectĂşan inventarios sistemáticos y exhaustivos de la flora en cinco cuadrĂculas U.T.M. (Datum ED50) de 1 Ă— 1 km, que forman un transecto NNE-SSW en la regiĂłn natural de La Alcarria Occidental y Mesa de Ocaña, con objeto de conocer la composiciĂłn en plantas vasculares. El mĂ©todo, aplicado en el año agrolĂłgico 2002 03, hace posible considerar el contingente florĂstico determinado como relativamente prĂłximo a la riqueza total y permite efectuar una comparaciĂłn geográfica de la diversidad en tĂ©rminos absolutos. Se efectĂşa un análisis de la semejanza florĂstica y se analizan comparativamente los espectros taxonĂłmicos. Los resultados muestran elevados niveles de riqueza en todos los niveles espaciales (1634 taxones autĂłctonos, a escala regional; 690, en el conjunto de las cinco cuadrĂculas; y una media superior a 400 por cuadrĂcula). De mayor a menor, el espectro tĂpico de la regiĂłn presenta un dominio de compuestas, gramĂneas, leguminosas, crucĂferas y cariofiláceas; aunque las cuadrĂculas presentan ligeras variaciones a este modelo.On a fait des relevĂ©s systĂ©matiques et exhaustives de la flore á cinq carrĂ©s UTM de 1 km Ă— 1 km (ED50), formant un transect NNE-SSW dans la rĂ©gion naturelle de La Alcarria de l’Ouest et “Mesa” d’Ocaña (Centre de la PĂ©ninsule IbĂ©rique), avec le but de connaĂ®tre la composition en plantes vasculaires. La mĂ©thode, appliquĂ©e dans l’annĂ©e agrologique 2002-03, permet d’envisager l’ensemble vasculair dĂ©terminĂ© comme relativement proche Ă la richesse totale, ce qui fair possible la comparaison gĂ©ographique de la diversitĂ© floristique en termes absolus. En mĂŞme temps, on a effectuĂ© une analyse de similaritĂ© de la flore entre les cinq unitĂ©s rĂ©gulières et une analyse comparative des spectres taxonomiques. Les rĂ©sultats montrent des niveaux Ă©levĂ©s de la richesse Ă tous les espaces Ă©tudiĂ©s (1634 espèces, au niveau rĂ©gional, 690 dans l´ensemble des cinq carrĂ©s UTM, et une moyenne supĂ©rieure Ă 400 par carrĂ©). Du plus haut au plus bas, le spectre typique de la rĂ©gion est domainĂ© par les composĂ©es, suivies par les graminĂ©es, lĂ©gumineuses, crucifères et caryophyllacĂ©es, bien que les unitĂ©s de 1 km² ont de lĂ©gères variations de ce modèle
Sobre la presencia actual de Atropa baetica Willk. (Solanaceae) en la Alta Alcarria (Utande, Guadalajara)
Se da noticia del hallazgo y presencia actual de Atropa baetica
Willk. (Solanaceae) en una localidad de la Alta Alcarria, municipio de Utande
(provincia de Guadalajara). Se indica la cuadrĂcula U.T.M. de 1Ă—1 km donde se
encuentra. Se presentan datos ecológicos y fitogeográficos básicos sobre su
emplazamiento, algunos datos vegetativos descriptivos de los dos rodales o
conjuntos de tallos encontrados y comentarios sobre posibles amenazas de la
escueta poblaciĂłn. Finalmente, se comenta la relaciĂłn de esta nueva cita con
referencias antiguas publicadas en la bibliografĂa.On the actual presence of Atropa baetica Willk. (Solanaceae)
in the Alta Alcarria (Utande, Guadalajara). This article reports the finding and
current presence of Atropa baetica Willk. (Solanaceae) in a municipality (Utande)
belonging to the “Alta Alcarria” (province of Guadalajara, Central Iberian
Peninsula, Spain). We provide the 1Ă—1 km U.T.M.-grid location. Ecological and
phytogeographical data as well as information on the location basic vegetative
descriptive data from the two sets of stems and a review on the possible threats are
reported. Finally, the relationshio of this new citation with old literature references
is discussed
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