Avaliação do desempenho energético de um grande edifício de serviços existente

Nesta dissertação pretende-se caracterizar o desempenho energético de um grande edifício de serviços existente, da tipologia ensino, avaliar e identificar potenciais medidas que melhorem aquele desempenho, permitindo, em complemento, determinar a sua classificação energética no âmbito da legislação vigente. A pertinência do estudo prende-se com a avaliação do desempenho energético dos edifícios e com o estudo de medidas de melhoria que permitam incrementar a eficiência energética, por recurso a um programa de simulação energética dinâmica certificado – DesignBuilder e tendo em conta a regulamentação portuguesa em vigor. Inicialmente procedeu-se à modelação do edifício com recurso ao programa DesignBuilder, e, simultaneamente, realizou-se um levantamento de todas as suas características ao nível de geometria, pormenores construtivos, sistemas AVAC e de iluminação e fontes de energia utilizadas. Com vista à caracterização do modo de operação do edifício, foi realizado um levantamento dos perfis reais de utilização em termos de ocupação, iluminação e equipamentos para os vários espaços. Foram realizadas medições de caudais de ar novo e da temperatura do ar, em alguns equipamentos e alguns espaços específicos. Foram realizadas medições em tempo real e leituras de contagens da energia eléctrica utilizada, quer em período de aulas quer em período de férias, que permitiram a desagregação das facturas da energia eléctrica que se apresentam globais para o campus do ISEP. Foram realizadas leituras de contagens de gás natural. Em sequência, foi realizada a simulação energética dinâmica com o intuito de ajustar o modelo criado aos consumos reais e de analisar medidas de melhoria que lhe conferissem um melhor desempenho energético. Essas medidas são agrupadas em quatro tipos: - Medidas de natureza comportamental; - Medidas de melhoria da eficiência energética nos sistemas de iluminação; - Medidas de melhoria de eficiência energética nos sistemas AVAC;- Medidas que visam a introdução de energias de fonte renovável; Em sequência, foi elaborada a simulação nominal e calculados os indicadores de eficiência energética com vista à respectiva classificação energética do edifício, tendo o edifício apresentado uma Classe Energética D de acordo com a escala do SCE. Finalmente, foi avaliado o impacto das diferentes medidas de melhoria identificadas e com potencial de aplicação, isto é, que apresentaram um retorno simples do investimento inferior a oito anos, tanto ao nível do desempenho energético real do edifício, como ao nível da sua classificação energética. De onde se concluiu que existe um potencial de 7% de redução nos consumos energéticos actuais do edifício e de 18% se o funcionamento do edifício for em pleno, ou seja, se todos os seus sistemas estiverem efectivamente em funcionamento, e que terá impacto na classificação energética alcançado uma Classe Energética C.

Spectral solution for the air stripping pollutants removal dynamic model with non linear steady state conditions

This work deals with the numerical simulation of air stripping process for the pre-treatment of groundwater used in human consumption. The model established in steady state presents an exponential solution that is used, together with the Tau Method, to get a spectral approach of the solution of the system of partial differential equations associated to the model in transient state.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

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.

Dynamic stability metrics for the container loading problem

The Container Loading Problem (CLP) literature has traditionally evaluated the dynamic stability of cargo by applying two metrics to box arrangements: the mean number of boxes supporting the items excluding those placed directly on the floor (M1) and the percentage of boxes with insufficient lateral support (M2). However, these metrics, that aim to be proxies for cargo stability during transportation, fail to translate real-world cargo conditions of dynamic stability. In this paper two new performance indicators are proposed to evaluate the dynamic stability of cargo arrangements: the number of fallen boxes (NFB) and the number of boxes within the Damage Boundary Curve fragility test (NB_DBC). Using 1500 solutions for well-known problem instances found in the literature, these new performance indicators are evaluated using a physics simulation tool (StableCargo), replacing the real-world transportation by a truck with a simulation of the dynamic behaviour of container loading arrangements. Two new dynamic stability metrics that can be integrated within any container loading algorithm are also proposed. The metrics are analytical models of the proposed stability performance indicators, computed by multiple linear regression. Pearson’s r correlation coefficient was used as an evaluation parameter for the performance of the models. The extensive computational results show that the proposed metrics are better proxies for dynamic stability in the CLP than the previous widely used metrics.