Trolleybuses and electric mini-buses in the Portuguese city of Coimbra are one of the main forms of daily transportation of its many citizens. As part of CIVITAS MODERN European Project MObility, Development and Energy use ReductioN, one of its main objectives for Coimbra is the integration of clean production electricity system owned by the City Council, able to supply the energy to the trolleybus traction lines, plus electric energy to charge the batteries of the electric mini-buses fleet. This electric fleet is undergoing a significant expansion in the near future. A study was carried out in order to evaluate the potential of renewable energy production to supply the electric fleet public transportation in Coimbra, reducing the necessity of fossil fuels and associated emissions, therefore improving the air quality. The electricity source will be a low head hydro potential, using an already existing dam-bridge, where a group of turbine-generators units can be placed, with modest operation costs and reduced civil works with small environmental impact. The optimization of the renewable energy generation is also assessed as a function of the load profiles.
In Brazil, the electric power distributors must buy electricity on auctions one, three and five years ahead. If there is inefficiency in the contracting of electric energy, the chamber of Commercialization of Electric Energy, which enables the commercialization, can apply penalties. Thus, this paper proposes a computational approach to forecasting electricity by the class of the consumer using a multi-layer perceptron artificial neural network with a backpropagation algorithm and a prediction using time series techniques through the Bayesian and Akaike selection criteria. The forecast of electricity consumption can serve as support in the purchase of electricity in auctions in the regulated contracting environment and in the process of settlement of differences and for energy management, customer service, and distributor billing. The results show that a multilayer network with a backpropagation algorithm is able to learn the behavior of the data that influences the electric energy consumed by consumption class and can be used to follow the evolution in the demand of each class of consumption and, consequently, to help distributors in the process of contracting of electricity, reduce losses like fines, and reduce the costs of the energy distributor.
Este trabalho estuda o problema clássico de agendamento da produção em job shop para minimização do makespan. Devido à natureza combinatória e complexidade computacional desse problema, o uso de técnicas metaheurísticas aliadas a métodos de busca local é bastante difundido por possibilitar resultados satisfatórios em um tempo computacional viável. Em geral, os métodos de busca local se baseiam em permutações empíricas das operações que compõe o caminho crítico de uma solução, as chamadas operações críticas, o que demanda o cálculo do caminho crítico para cada uma das soluções geradas no processo de busca. Além de elevar o custo computacional da busca local, tal abordagem promove permutações de operações que não resultam em qualquer melhoria da solução. Este trabalho investiga a distribuição das operações críticas nas máquinas e a correlação entre essa distribuição e características estatísticas dos problemas. O objetivo é estimar máquinas que concentram operações críticas e identificar características que possam contribuir para definição de métodos de busca local que não dependam do cálculo do caminho crítico a cada solução. Experimentos computacionais com instâncias usuais da literatura mostram que há uma concentração de operações críticas em algumas máquinas e, em alguns casos, uma correlação positiva significativa entre essa concentração e os tempos médios de processamento das operações, o que pode fornecer subsídios para criação de métodos de busca local computacionalmente mais eficientes.
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