Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Dual source/dual energy-renal applications

Development of Dual Source Computed Tomography (Definition, Siemens Medical Solutions, Erlanger, Germany) allowed advances in temporal resolution, with the addition of a second X-ray source and an array of detectors to the TCM 64 slices. The ability to run exams on Dual Energy, allows greater differentiation of tissues, showing differences between closer attenuation coefficients. In terms of renal applications, the distinction of kidney stones and masses become one of the main advantages of the use of dual-energy technology. This article pretends to demonstrate operating principles of this equipment, as its main renal applications.

Combining artificial intelligence techniques for the training of power system control centre operators

Control Centre operators are essential to assure a good performance of Power Systems. Operators’ actions are critical in dealing with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in incident analysis and diagnosis, and service restoration of Power Systems, offering context awareness and an easy integration in the working environment.

Espresso coffee residues: a valuable source of unextracted compounds

Espresso spent coffee grounds were chemically characterized to predict their potential, as a source of bioactive compounds, by comparison with the ones from the soluble coffee industry. Sampling included a total of 50 samples from 14 trademarks, collected in several coffee shops and prepared with distinct coffee machines. A high compositional variability was verified, particularly with regard to such water-soluble components as caffeine, total chlorogenic acids (CGA), and minerals, supported by strong positive correlations with total soluble solids retained. This is a direct consequence of the reduced extraction efficiency during espresso coffee preparation, leaving a significant pool of bioactivity retained in the extracted grounds. Besides the lipid (12.5%) and nitrogen (2.3%) contents, similar to those of industrial coffee residues, the CGA content (478.9 mg/100 g), for its antioxidant capacity, and its caffeine content (452.6 mg/100 g), due to its extensive use in the food and pharmaceutical industries, justify the selective assembly of this residue for subsequent use.

Efeito das condições ambientais na produtividade lipídica da Dunaliella tertiolecta

Este trabalho teve como objectivo a optimização das condições de crescimento de biomassa algal tendo em vista a sua utilização como fonte de lípidos para biocombustíveis. Assim, procedeu-se à inoculação de duas estirpes, a Dunaliella tertiolecta (água salgada) e a Tetraselmis subcordiformis (água salobra), seleccionando-se a Dunaliella tertiolecta uma vez que esta apresentou um crescimento mais rápido. Escolhida a estirpe a usar, avaliou-se a influência da composição do meio de cultura da espécie, variando-se a concentração de macronutrientes (Magnésio, Potássio, Azoto, Fósforo) e de micronutrientes (Manganês, Zinco, Ferro, Cobalto) presentes no meio em 10 e 20 vezes, comparativamente à do meio de cultura padrão, o meio Artificial Seawater Medium with Vitamins. Avaliou-se o crescimento algal, a uma temperatura de 25 ºC ± 2 ºC, com uma intensidade de iluminação de 5000 lux (lâmpadas luz dia) e fotoperíodos 12:12 h, controlando possíveis contaminações nas culturas em estudo. Para os ensaios realizados com a Dunaliella tertiolecta, os melhores resultados para a produtividade média e máxima de biomassa, 63,06 mgbiomassa seca/L.dia e 141,79 mgbiomassa seca/L.dia, respectivamente, foram obtidos no ensaio em que se fez variar 10 vezes a concentração de azoto (sob a forma de nitrato). Os resultados mais satisfatórios para o teor lípidico e para a produtividade lipídica máxima, 33,45% e 47,43 mgóleo/L.dia respectivamente, também foram obtidos no ensaio em que se fez variar 10 vezes a concentração de azoto (sob a forma de nitrato), (com extracção dos lípidos usando o método de Bligh e Dyer). Foram testados dois solventes para a extracção de lipídos, o clorofórmio e o hexano, tendose obtido resultados superiores com o clorofórmio, comparativamente aos obtidos quando se usou hexano, com excepção do ensaio em que se aumentou 20 vezes a concentração de fósforo no meio de cultura das microalgas. Verificou-se que, em todos os ensaios foi atingido o estado estacionário sensivelmente na mesma altura, isto é, decorridos cerca de 25 dias após o início do estudo, excepto os ensaios em que se fez variar a concentração de cobalto, para os quais as culturas não se adaptaram às alterações do meio, acabando por morrer passados 15 dias. A adição dos macronutrientes e micronutrientes usados nos ensaios, nas quantidades testadas, não influenciou significativamente a produtividade lipídica, com excepção do azoto e ferro. Conclui-se que o aumento da concentração de azoto para 10x o valor padrão potencia o aumento da produtividade lipídica máxima para mais do dobro (3,6 vezes – Padrão: 13,25 mgóleo/L.dia; 10x N: 47,43 mgóleo/L.dia) e que o aumento da concentração de ferro para 10x o valor padrão potencia o aumento da produtividade lipídica máxima para aproximadamente o dobro (1,9 vezes - Padrão: 14,61 mgóleo/L.dia; 10x Fe: 28,04 mgóleo/L.dia). Nos ensaios realizados com adição de azoto ou ferro, os resultados obtidos para a concentração, teor lípidico e produtividade lipídica máxima, foram sempre superiores aos do padrão correspondente, pelo que se pode concluir que estes ensaios se apresentam como os mais promissores deste estudo, embora o ensaio mais satisfatório tenha sido aquele em que se promoveu a alteração da concentração de azoto para 10 vezes o valor padrão.

Exploring bioactive properties of marine cyanobacteria Isolated from the Portuguese coast: high potential as a source of anticancer compounds

The oceans remain a major source of natural compounds with potential in pharmacology. In particular, during the last few decades, marine cyanobacteria have been in focus as producers of interesting bioactive compounds, especially for the treatment of cancer. In this study, the anticancer potential of extracts from twenty eight marine cyanobacteria strains, belonging to the underexplored picoplanktonic genera, Cyanobium, Synechocystis and Synechococcus, and the filamentous genera, Nodosilinea, Leptolyngbya, Pseudanabaena and Romeria, were assessed in eight human tumor cell lines. First, a crude extract was obtained by dichloromethane:methanol extraction, and from it, three fractions were separated in a Si column chromatography. The crude extract and fractions were tested in eight human cancer cell lines for cell viability/toxicity, accessed with the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) and lactic dehydrogenase release (LDH) assays. Eight point nine percent of the strains revealed strong cytotoxicity; 17.8% showed moderate cytotoxicity, and 14.3% assays showed low toxicity. The results obtained revealed that the studied genera of marine cyanobacteria are a promising source of novel compounds with potential anticancer activity and highlight the interest in also exploring the smaller filamentous and picoplanktonic genera of cyanobacteria.

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Open-ZB: an open-source implementation of the IEEE 802.15.4/ZigBee protocol stack on TinyOS

The IEEE 802.15.4/ZigBee protocols are gaining increasing interests in both research and industrial communities as candidate technologies for Wireless Sensor Network (WSN) applications. In this paper, we present an open-source implementation of the IEEE 802.15.4/Zigbee protocol stack under the TinyOS operating system for the MICAz motes. This work has been driven by the need for an open-source implementation of the IEEE 802.15.4/ZigBee protocols, filling a gap between some newly released complex C implementations and black-box implementations from different manufacturers. In addition, we share our experience on the challenging problem that we have faced during the implementation of the protocol stack on the MICAz motes. We strongly believe that this open-source implementation will potentiate research works on the IEEE 802.15.4/Zigbee protocols allowing their demonstration and validation through experimentation.

Sistema de detecção e localização de veículos subaquáticos

Mestrado em Engenharia Electrotécnica e de Computadores.Área de Especialização de Sistemas Autónomos

Recomendação de conteúdos multimédia

A quantidade e variedade de conteúdos multimédia actualmente disponíveis cons- tituem um desafio para os utilizadores dado que o espaço de procura e escolha de fontes e conteúdos excede o tempo e a capacidade de processamento dos utilizado- res. Este problema da selecção, em função do perfil do utilizador, de informação em grandes conjuntos heterogéneos de dados é complexo e requer ferramentas específicas. Os Sistemas de Recomendação surgem neste contexto e são capazes de sugerir ao utilizador itens que se coadunam com os seus gostos, interesses ou necessidades, i.e., o seu perfil, recorrendo a metodologias de inteligência artificial. O principal objectivo desta tese é demonstrar que é possível recomendar em tempo útil conteúdos multimédia a partir do perfil pessoal e social do utilizador, recorrendo exclusivamente a fontes públicas e heterogéneas de dados. Neste sen- tido, concebeu-se e desenvolveu-se um Sistema de Recomendação de conteúdos multimédia baseado no conteúdo, i.e., nas características dos itens, no historial e preferências pessoais e nas interacções sociais do utilizador. Os conteúdos mul- timédia recomendados, i.e., os itens sugeridos ao utilizador, são provenientes da estação televisiva britânica, British Broadcasting Corporation (BBC), e estão classificados de acordo com as categorias dos programas da BBC. O perfil do utilizador é construído levando em conta o historial, o contexto, as preferências pessoais e as actividades sociais. O YouTube é a fonte do histo- rial pessoal utilizada, permitindo simular a principal fonte deste tipo de dados - a Set-Top Box (STB). O historial do utilizador é constituído pelo conjunto de vídeos YouTube e programas da BBC vistos pelo utilizador. O conteúdo dos vídeos do YouTube está classificado segundo as categorias de vídeo do próprio YouTube, sendo efectuado o mapeamento para as categorias dos programas da BBC. A informação social, que é proveniente das redes sociais Facebook e Twit- ter, é recolhida através da plataforma Beancounter. As actividades sociais do utilizador obtidas são filtradas para extrair os filmes e séries que são, por sua vez, enriquecidos semanticamente através do recurso a repositórios abertos de dados interligados. Neste caso, os filmes e séries são classificados através dos géneros da IMDb e, posteriormente, mapeados para as categorias de programas da BBC. Por último, a informação do contexto e das preferências explícitas, através da classificação dos itens recomendados, do utilizador são também contempladas. O sistema desenvolvido efectua recomendações em tempo real baseado nas actividades das redes sociais Facebook e Twitter, no historial de vídeos Youtube e de programas da BBC vistos e preferências explícitas. Foram realizados testes com cinco utilizadores e o tempo médio de resposta do sistema para criar o conjunto inicial de recomendações foi 30 s. As recomendações personalizadas são geradas e actualizadas mediante pedido expresso do utilizador.

Towards Active Course Marks for Autonomous Sailing Competitions

This paper describes the environmental monitoring / regatta beacon buoy under development at the Laboratory of Autonomous Systems (LSA) of the Polytechnic Institute of Porto. On the one hand, environmentalmonitoring of open water bodies in real or deferred time is essential to assess and make sensible decisions and, on the other hand, the broadcast in real time of position, water and wind related parameters allows autonomous boats to optimise their regatta performance. This proposal, rather than restraining the boats autonomy, fosters the development of intelligent behaviour by allowing the boats to focus on regatta strategy and tactics. The Nautical and Telemetric Application (NAUTA) buoy is a dual mode reconfigurable system that includes communications, control, data logging, sensing, storage and power subsystems. In environmental monitoring mode, the buoy gathers and stores data from several underwater and above water sensors and, in regatta mode, the buoy becomes an active course mark for the autonomous sailing boats in the vicinity. During a race, the buoy broadcasts its position, together with the wind and the water current local conditions, allowing autonomous boats to navigate towards and round the mark successfully. This project started with the specification of the requirements of the dual mode operation, followed by the design and building of the buoy structure. The research is currently focussed on the development of the modular, reconfigurable, open source-based control system. The NAUTA buoy is innovative, extensible and optimises the on board platform resources.

Environmental Decision Support: a Distributed Artificial Intelligence Approach

Decision making in any environmental domain is a complex and demanding activity, justifying the development of dedicated decision support systems. Every decision is confronted with a large variety and amount of constraints to satisfy as well as contradictory interests that must be sensibly accommodated. The first stage of a project evaluation is its submission to the relevant group of public (and private) agencies. The individual role of each agency is to verify, within its domain of competence, the fulfilment of the set of applicable regulations. The scope of the involved agencies is wide and ranges from evaluation abilities on the technical or economical domains to evaluation competences on the environmental or social areas. The second project evaluation stage involves the gathering of the recommendations of the individual agencies and their justified merge to produce the final conclusion. The incorporation and accommodation of the consulted agencies opinions is of extreme importance: opinions may not only differ, but can be interdependent, complementary, irreconcilable or, simply, independent. The definition of adequate methodologies to sensibly merge, whenever possible, the existing perspectives while preserving the overall legality of the system, will lead to the making of sound justified decisions. The proposed Environmental Decision Support System models the project evaluation activity and aims to assist developers in the selection of adequate locations for their projects, guaranteeing their compliance with the applicable regulations.

Multi-elemental analysis of ready-to-eat “baby leaf” vegetables using microwave digestion and high-resolution continuum source atomic absorption spectrometry

The mineral content (phosphorous (P), potassium (K), sodium (Na), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn) and copper (Cu)) of eight ready-to-eat baby leaf vegetables was determined. The samples were subjected to microwave-assisted digestion and the minerals were quantified by High-Resolution Continuum Source Atomic Absorption Spectrometry (HR-CS-AAS) with flame and electrothermal atomisation. The methods were optimised and validated producing low LOQs, good repeatability and linearity, and recoveries, ranging from 91% to 110% for the minerals analysed. Phosphorous was determined by a standard colorimetric method. The accuracy of the method was checked by analysing a certified reference material; results were in agreement with the quantified value. The samples had a high content of potassium and calcium, but the principal mineral was iron. The mineral content was stable during storage and baby leaf vegetables could represent a good source of minerals in a balanced diet. A linear discriminant analysis was performed to compare the mineral profile obtained and showed, as expected, that the mineral content was similar between samples from the same family. The Linear Discriminant Analysis was able to discriminate different samples based on their mineral profile.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.