Avaliação de imóveis históricos

O objectivo da presente dissertação é o estudo e desenvolvimento de um modelo de avaliação de imóveis históricos: os edifícios classificados como Monumentos Nacionais - “MN”, os Imóveis de Interesse Público - “IIP”, os imóveis de Interesse Municipal - “IIM”, e todos os imóveis cuja arquitectura e história os façam distinguir pela sua singularidade e marco, enquanto memória a preservar. A investigação realizada, permitiu compreender a forma, como são actualmente avaliados os imóveis históricos. A pesquisa incidiu no património edificado nacional, considerando as respectivas características, que os definem como imóveis a preservar. Foi observado um conjunto de imóveis classificados pelo Estado, e enfatizados os conceitos de arquitectura, história e património, considerados como parte essencial do processo de avaliação. O estado de negligência, em Portugal, a que está votado parte significativa do património edificado, com raras excepções, foi igualmente focado, analisando-se as razões desta situação. Realizou-se a aplicação do Método do Custo de Reprodução a um edifício classificado como Monumento Nacional, e actualmente em estado devoluto, o “Forte de Nossa Senhora da Graça”, localizado em Elvas. Os resultados obtidos, foram comparados através da aplicação do Método de Actualização das Rendas Futuras, por seriação de critérios e pela análise dos valores obtidos. Os métodos de avaliação utilizados foram comparados entre si, procurando-se estabelecer um modelo baseado em critérios de avaliação, que revele o impacto histórico e arquitectónico, numa perspectiva social. Foram expostas as premissas nas quais assentam este tipo de avaliações imobiliárias e retiradas conclusões sobre os resultados obtidos.

Projecto de AVAC de um edifício com multiutilização

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

The daily and hourly energy consumption and load forecasting using artifitial neural network method: a case study using a set of 93 households in Portugal

It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.

Algorithm-oriented design of efficient many-core architectures applied to dense matrix multiplication

Recent integrated circuit technologies have opened the possibility to design parallel architectures with hundreds of cores on a single chip. The design space of these parallel architectures is huge with many architectural options. Exploring the design space gets even more difficult if, beyond performance and area, we also consider extra metrics like performance and area efficiency, where the designer tries to design the architecture with the best performance per chip area and the best sustainable performance. In this paper we present an algorithm-oriented approach to design a many-core architecture. Instead of doing the design space exploration of the many core architecture based on the experimental execution results of a particular benchmark of algorithms, our approach is to make a formal analysis of the algorithms considering the main architectural aspects and to determine how each particular architectural aspect is related to the performance of the architecture when running an algorithm or set of algorithms. The architectural aspects considered include the number of cores, the local memory available in each core, the communication bandwidth between the many-core architecture and the external memory and the memory hierarchy. To exemplify the approach we did a theoretical analysis of a dense matrix multiplication algorithm and determined an equation that relates the number of execution cycles with the architectural parameters. Based on this equation a many-core architecture has been designed. The results obtained indicate that a 100 mm(2) integrated circuit design of the proposed architecture, using a 65 nm technology, is able to achieve 464 GFLOPs (double precision floating-point) for a memory bandwidth of 16 GB/s. This corresponds to a performance efficiency of 71 %. Considering a 45 nm technology, a 100 mm(2) chip attains 833 GFLOPs which corresponds to 84 % of peak performance These figures are better than those obtained by previous many-core architectures, except for the area efficiency which is limited by the lower memory bandwidth considered. The results achieved are also better than those of previous state-of-the-art many-cores architectures designed specifically to achieve high performance for matrix multiplication.