Abstract-An Energy Management System (EMS) is a monitoring tool that tracks buildings energy consumption with the purpose of enhancing energy efficiency, by identifying savings opportunities and misuse situations. To achieve this, EMSs collect data flowsdata streams-from a network of energy meters and sensors, which are then combined into useful information. Data must be processed in real-time, to support a timely decision making process. I. IntroductionBuildings account for 40% of energy consumption, ahead of other sectors, such as industry or transportation [1]. Therefore, small improvements on building energy consumption translate to major savings. Among other ways, energy efficiency in buildings can be achieved through Intelligent Energy Management [2]. This topic refers to monitoring energy consumption, and the careful tracing of energy usage enabling building managers to identify saving opportunities. EMSs continuously monitor the energy consumed in buildings. Consumption related data is evaluated according to several variables such as time, areas and their occupation, equipment state, expected consumption, among others, which determines the building energy usage patterns, providing required information to determine the adjustments towards improving energy usage [3].One fundamental aspect of energy management is timeliness: faster decisions translate to less waste and larger savings. In other words, timely information greatly improves the decision making process [4] since building managers are able to immediately diagnose and promptly respond to anomalous situations. EMSs are real-time decision making applications that require (near) real-time integration of huge quantities of data, wherein each record relates to a very short period [5], thus leading to Big Data challenges: (i) achieve low latency on queries evaluation,

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RESUMO -Existe hoje uma grande preocupação em relação ao grau de tratamento dos efluentes gerados pela população e indústrias, as suas consequências sobre o meio ambiente, a qualidade das águas e aos seus usos e benefícios. As Estações de Tratamento de Águas Residuais (ETAR), são grandes sistemas não-lineares sujeitos a grandes perturbações na taxa de fluxo de entrada das estações e na carga de poluentes, juntamente com incertezas quanto à composição das águas residuais recebidas. O presente trabalho tem como objetivo modelar, simular e validar um metamodelo do processo químico de tratamento de efluentes. O modelo rigoroso foi implementado no ambiente SIMULINK/C por Alex et al. (2008), e o metamodelo foi gerado utilizando a metodologia Kriging. Para a geração de variáveis aleatórias nãoenviesadas que fazem parte da construção do metamodelo, foi utilizada a técnica do hipercubo latino. Foi observado que todas as variáveis de qualidade do efluente (saídas), estão dentro dos valores permitidos. Os resultados obtidos no metamodelo foram comparados, por meio de validação cruzada pelos gráficos de dispersão, com o modelo rigoroso, apresentando-se o melhor ajuste das curvas possíveis. Por fim, conclui-se que o metamodelo gerado tem a capacidade de prever as variáveis de decisão do processo, podendo substituir o modelo rigoroso, gerando resultados em menor tempo hábil.

The treatment of waste-water is a constant concern of the modern world, given the inherent enhancement of human-life quality and wellbeing through public health improvement. The present work aims at optimizing a state-of-art waste-water treatment plant model and to propose different co-generation process configurations with the purpose of recovering energy by means of burning the biogas produced by the plant and, consequently, reducing its overall operational costs. The optimization took into account disturbances of the waste-water treatment plant feed in order to meet environmental regulations. The results from the co-generation implementation show that a reduction of up to 73% of the electrical power consumption, and all of the heating required by this plant model.

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