Os montes da serra de Tavira

The human occupation of the Tavira ridge is characterised by a network of small agglomerations of buildings (known as ‘montes’) belonging to the proprietors of lands around them. This way of organising places is, by and large, extensive to an important area of the schist mountains that stretch from Alentejo to Algarve, from the Guadiana river to the Atlantic Ocean. However, within the scope of that larger entity, the Tavira ridge appears as a sub-unit with traits of its own, which may be attributed not only to biophysical conditions, but also to the historical circumstances associated to the settlement of the territory. This particular ridge, which was confined to the perimeter of the Tavira municipality during the days of the Old Regime, has been more densely populated, due to its lack of interior hierarchisation, unlike what happens in neighbouring sub-units, both in terms of agglomeration sizes and of the property system itself. The traditional architecture of these areas was usually reduced to the bare essentials, reflecting the adverse conditions that generally surrounded the local economies. As can easily be gleaned from the plans shown here, the groupings of edifices that define the ridge’s settlement are largely characterised by the aggregation of buildings belonging to various owners, often intent on achieving a prominent position in the landscape.

Intelligent predictive control for thermal comfort and energy savings in public buildings

This talk addresses the problem of controlling a heating ventilating and air conditioning system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most operating conditions are conflicting goals requiring some sort of optimisation method to find appropriate solutions over time. In this work a discrete model based predictive control methodology is applied to the problem. It consists of three major components: the predictive models, implemented by radial basis function neural networks identifed by means of a multi-objective genetic algorithm [1]; the cost function that will be optimised to minimise energy consumption and provide adequate thermal comfort; and finally the optimisation method, in this case a discrete branch and bound approach. Each component will be described, with a special emphasis on a fast and accurate computation of the PMV indices [2]. Experimental results obtained within different rooms in a building of the University of Algarve will be presented, both in summer [3] and winter [4] conditions, demonstrating the feasibility and performance of the approach. Energy savings resulting from the application of the method are estimated to be greater than 50%.

Model comparison for temperature estimation inside buildings

This paper presents a comparison between a physical model and an artificial neural network model (NN) for temperature estimation inside a building room. Despite the obvious advantages of the physical model for structure optimisation purposes, this paper will test the performance of neural models for inside temperature estimation. The great advantage of the NN model is a big reduction of human effort time, because it is not needed to develop the structural geometry and structural thermal capacities and to simulate, which consumes a great human effort and great computation time. The NN model deals with this problem as a “black box” problem. We describe the use of the Radial Basis Function (RBF), the training method and a multi-objective genetic algorithm for optimisation/selection of the RBF neural network inputs and number of neurons.

Using data envelopment analysis to assess the efficiency of social security systems: the case of income protection in old age

As the number of pensioners in Europe rises relative to the number of people in employment, the gap between the contributions and the benefit levels increases, and consequently ensuring adequate pensions on a sustainable basis has become a major challenge. This study aims to explore the potential of using the Data Envelopment Analysis (DEA) technique in order to access the efficiency of the income protection in old age, one of the most important branches of Social Security. To this effect, we collected data from the 27 European Union Member States regarding this branch. Our results show important differences among the Member States and stress the importance of identifying best practices to achieve more adequate, sustainable and modernised pension systems. Our results also highlight the importance of using DEA as a decision support tool for policy makers.

Predictive control of HVAC systems in buildings

Dissertação de Mestrado, Engenharia Eletrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015