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 Andalucía 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

Biogeografía urbana: presentación del caso de Alcalá de Henares.

Sin resume

Geografía de las Plantas en La Alcarria Occidental y Mesa de Ocaña (I). Análisis florístico 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 × 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 efectúan inventarios sistemáticos y exhaustivos de la flora en cinco cuadrículas U.T.M. (Datum ED50) de 1 × 1 km, que forman un transecto NNE-SSW en la región natural de La Alcarria Occidental y Mesa de Ocaña, con objeto de conocer la composición en plantas vasculares. El método, aplicado en el año agrológico 2002 03, hace posible considerar el contingente florístico determinado como relativamente próximo a la riqueza total y permite efectuar una comparación geográfica de la diversidad en términos absolutos. Se efectúa un análisis de la semejanza florística y se analizan comparativamente los espectros taxonómicos. Los resultados muestran elevados niveles de riqueza en todos los niveles espaciales (1634 taxones autóctonos, a escala regional; 690, en el conjunto de las cinco cuadrículas; y una media superior a 400 por cuadrícula). De mayor a menor, el espectro típico de la región presenta un dominio de compuestas, gramíneas, leguminosas, crucíferas y cariofiláceas; aunque las cuadrículas presentan ligeras variaciones a este modelo.On a fait des relevés systématiques et exhaustives de la flore á cinq carrés UTM de 1 km × 1 km (ED50), formant un transect NNE-SSW dans la région naturelle de La Alcarria de l’Ouest et “Mesa” d’Ocaña (Centre de la Péninsule Ibérique), avec le but de connaître la composition en plantes vasculaires. La méthode, appliquée dans l’année agrologique 2002-03, permet d’envisager l’ensemble vasculair déterminé comme relativement proche à la richesse totale, ce qui fair possible la comparaison géographique de la diversité floristique en termes absolus. En même temps, on a effectué une analyse de similarité de la flore entre les cinq unités régulières et une analyse comparative des spectres taxonomiques. Les résultats montrent des niveaux élevés de la richesse à tous les espaces étudiés (1634 espèces, au niveau régional, 690 dans l´ensemble des cinq carrés UTM, et une moyenne supérieure à 400 par carré). Du plus haut au plus bas, le spectre typique de la région est domainé par les composées, suivies par les graminées, légumineuses, crucifères et caryophyllacées, bien que les unités de 1 km² ont de légères 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 cuadrícula U.T.M. de 1×1 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 población. Finalmente, se comenta la relación de esta nueva cita con referencias antiguas publicadas en la bibliografía.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 1×1 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