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.

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.

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.

O efeito da aplicação da Estimulação Elétrica Funcional na reabilitação em adultos com lesão medular completa. Revisão Sistemática

Introdução: A Lesão Medular (LM) é um dos mais devastadores e traumáticos eventos que um Ser Humano pode vivenciar do ponto de vista clínico e emocional, demonstrando-se fundamental a disponibilização de recursos específicos para que o indivíduo possa enfrentar e gerir a sua nova realidade da melhor maneira possível. Alguns estudos têm vindo a demonstrar os benefícios de programas de reabilitação com estimulação elétrica funcional (EEF). Portanto, é de importante relevância perceber os reais efeitos da intervenção na recuperação de indivíduos com este diagnóstico. Objetivo: Analisar as evidências de abordagens de aplicação de correntes de estimulação elétrica funcional (EEF) para coadjuvar na reabilitação em adultos com lesão medular completa. Métodos: Foi conduzida uma pesquisa dos artigos preferencialmente estudos randomized controlled trials RCT´s e estudos quasi-experimentais com os mesmos participantes foram admitidos complementarmente aos experimentais compreendidos entre 2004 e 2013, bem como as citações e as referências bibliográficas de cada estudo nas principais bases de dados de ciências da saúde (Elsevier – Science Direct, Highwire Press, PEDro, PubMed, Scielo Portugal, Clinical Key, B-on, Biomed Central, LILACS- Literatura Latino-Americana e do Caribe em Ciências da Saúde) com as palavras-chave: “spinal cord injuries”, “rehabilitation, electric stimulation funtional”, “FES”, “therapy” em todas as combinações possíveis. Os estudos RCT’s foram analisados independentemente por dois revisores quanto aos critérios de inclusão e qualidade dos estudos. Resultados: Dos 857 estudos identificados apenas sete foram incluídos. Destes, dois apresentaram um score 3/10, um apresentou 4/10, um apresentou um score 5/10. O score total bem como o preenchimento ou não de cada critério encontram-se detalhados na tabela 1 e organizados por ordem alfabética de autores. Todos os estudos incluíram indivíduos com Lesão Medular Completa, idades entre 16 e 68 anos com diagnóstico de acordo com a American Spinal Injury Association (ASIA).Os programas de intervenção dividiram-se em programas de programas de força, densidade mineral óssea, cardiorrespiratório e de atividade física. Dos estudos incluídos, cinco apresentaram melhorias na reabilitação funcional para o grupo experimental, demonstrando assim uma influência positiva da estimulação elétrica funcional em lesões medulares completas. Apenas dois estudos não apresentaram diferenças estatisticamente significativas com relevância clínica. Conclusão: Há uma tendência notória do benefício dos programas com EEF em pacientes com lesões medulares completas parece melhorar a capacidade cardiorrespiratória, a densidade mineral óssea, a força e atividade física, dos indivíduos. Contudo, mais estudos com elevada qualidade metodológica serão essenciais para conceber o real efeito da sua aplicação. Palavras-chave: lesão medular completa; estimulação elétrica funcional, randomized controlled trials, revisão sistemática.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Neural control of an autonomous robot

This article aims to apply the concepts associated with artificial neural networks (ANN) in the control of an autonomous robot system that is intended to be used in competitions of robots. The robot was tested in several arbitrary paths in order to verify its effectiveness. The results show that the robot performed the tasks with success. Moreover, in the case of arbitrary paths the ANN control outperforms other methodologies, such as fuzzy logic control (FLC).