http://hdl.handle.net/10366/135879 2026-04-23T04:05:42Z http://hdl.handle.net/10366/135895 This paper proposes an hybrid approach for short term solar intensity forecasting, which combines different forecasting methodologies with a clustering algorithm, which plays the role of data filter, in order to support the selection of the best data for training. A set of methodologies based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM), used for short term solar irradiance forecast, is implemented and compared in order to facilitate the selection of the most appropriate methods and respective parameters according to the available information and needs. Data from the Brazilian city of Florianópolis, in the state of Santa Catarina, has been used to illustrate the methods applicability and conclusions. The dataset comprises the years of 1990 to 1999 and includes four solar irradiance components as well as other meteorological variables, such as temperature, wind speed and humidity. Conclusions about the irradiance components, parameters and the proposed clustering mechanism are presented. The results are studied and analysed considering both efficiency and effectiveness of the results. The experimental findings show that the hybrid model, combining a SVM approach with a clustering mechanism, to filter the data used for training, achieved promising results, outperforming the approaches without clustering. 2017-09-06T00:00:00Z http://hdl.handle.net/10366/135894 In this paper we make a critical review of the existing technology in the smart cities and smart grid paradigms from the security perspective. First we summarize the findings about the evolution of renewable technology over time and in particular the benefits of a Cost reduction potential for solar and wind power in the period 2015-2025.  Then we build from existing sources to highlight different ways for efficiency improvement in solar panel solutions during 1975-2015. Next we analyze growth of the smart metering and smart grid technology in the world.  Also, the existing Blockchain solutions are critically reviewed in regard to cyber infrastructure security. From these findings we conclude that there is an increasing need for developing new Blockchain solutions in the smart grids ecosystem. 2017-09-26T00:00:00Z http://hdl.handle.net/10366/135892 Stress is one of the most important factors in traffic accidents. When the driver is in this mental state, their skills and abilities are reduced. In this paper, we propose an algorithm to estimate the optimal speed to minimize stress levels on upcoming road segments when driving. The prediction model is based on deep learning. The stress level estimation considers the previous driver's driving behavior before reaching the road section to be assessed, the road state (weather and traffic), and the previous drives made by the driver. We use this algorithm to build a speed assistant. The solution provides an optimum average speed for each road segment that minimizes the stress. A validation experiment has been conducted in a real setting using two different types of vehicles. The proposal is able to predict the stress levels given the average speed by 84.20% on average. On the other hand, the speed assistant reduces the stress levels (estimated from the driver’s heart rate signal) and the aggressiveness of driving regardless of the vehicle type. The proposed solution is implemented on Android mobile devices and uses a heart rate chest strap. 2017-09-06T00:00:00Z http://hdl.handle.net/10366/135893 Recently have been proposed different physical and cognitive rehabilitation system that allow people with some disabilities to improve and recover some lost capabilities. All these systems allow to carry out these therapies at home proving patients the possibility to accomplish a better rehabilitation, due to the fact that they can practice at home and in a more controlled environment. But, it is not so common that these systems include some social features that reduce the feeling of social isolation of the patients. Thus, in this paper we present an adaptation of a previous proposal including some multiuser therapies that try include some social features and other aspect related to videogames that increases the motivation and makes the treatment funny. 2017-09-06T00:00:00Z http://hdl.handle.net/10366/135891 A steadily growing pervasion of the energy distribution grid with communication technology is widely seen as an enabler for new computational coordination techniques for renewable, distributed generation as well as for bundling with controllable consumers. Smart markets will foster a decentralized grid management. One important task as prerequisite to decentralized management is the ability to group together in order to jointly gain enough suitable flexibility and capacity to assume responsibility for a specific control task in the grid. In self-organized smart grid scenarios, grouping or coalition formation has to be achieved in a decentralized and situation aware way based on individual capabilities. We present a fully decentralized coalition formation approach based on an established agent-based heuristics for predictive scheduling with the additional advantage of keeping all information about local decision base and local operational constraints private. Two closely interlocked optimization processes orchestrate an overall procedure that adapts a coalition structure to best suit a given set of energy products. The approach is evaluated in several simulation scenarios with different type of established models for integrating distributed energy resources and is also extended to the induced use case of surplus distribution using basically the same algorithm. 2017-09-06T00:00:00Z http://hdl.handle.net/10366/135890 The research presented in this paper is a thesis proposal with the main goal of defining an ontology comprising chosen organisational concepts applicable to large-scale multiagent systems (LSMAS), and building a metamodel for modelling selected organisational features in such systems. The method of applying aspects of human organisations to multiagent systems (MAS) comprising autonomous intelligent agents will be enriched through this research with a new perspective of modelling organisation dynamics in LSMAS. Results of this research, in their final version, will be tested using testbed scenarios based on a specific massively multi-player online role-playing game (MMORPG), since MMORPGs are one of the identified application domains of LSMAS. It is important to note that results described in this paper showcase partial results in their early stage of development. Nevertheless, first traces of a modelling tool that is expected to aid in development of LSMAS for numerous application domains, and ease their organisational design, are recognisable in the proposed combination of ontology engineering, metamodelling and code generating methods. 2017-09-06T00:00:00Z http://hdl.handle.net/10366/135889 The main goal of this work is to propose a new framework to design a novel family of mathematical models to simulate malware spreading in wireless sensor networks (WSNs). An analysis of the proposed models in the scientific literature reveals that the great majority are global models based on systems of ordinary differential equations such that they do not consider the individual characteristics of the sensors and their local interactions. This is a major drawback when WSNs are considered. Taking into account the main characteristics of WSNs (elements and topologies of network, life cycle of the nodes, etc.) it is shown that individual-based models are more suitable for this purpose than global ones. The main features of this new type of malware propagation models for WSNs are stated. 2017-09-06T00:00:00Z http://hdl.handle.net/10366/135888 2017-09-30T00:00:00Z http://hdl.handle.net/10366/135887 2017-09-30T00:00:00Z