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Artificial intelligence is at the forefront of a technological revolution, in particular as a key component to build autonomous agents. However, not only training such agents come at a great computational cost, but they also end up lacking human basic abilities like generalization, information extrapolation, knowledge transfer between contexts, or improvisation. To overcome current limitations, agents need a deeper understanding of their environment, and more efficiently learning it from data. There are very recent works that propose novel approaches to learn representations of the world: instead of learning invariant object encodings, they learn to isolate, or disentangle, the different variable properties which form an object. This would not only enable agents to understand object changes as modifications of one of their properties, but also to transfer such knowledge on the properties between different categories. This Master Thesis aims to develop a new machine learning model for disentangling object properties on monocular images of scenes. Our model is based on a state-of-the-art architecture for disentangled representations learning, and our goal is to reduce the computational complexity of the base model while also improving its performance. To achieve this, we will replace a recursive unsupervised segmentation network by an encoder-decoder segmentation network. Furthermore, before training such overparametrized neural model without supervision, we will profit from transfer learning of pre-trained weights from a supervised segmentation task. After developing a first vanilla model, we have tuned it to improve its performance and generalization capability. Then, an experimental validation has been performed on two commonly used synthetic datasets, evaluating both its disentanglement performance and computational efficiency, and on a more realistic dataset to analyze the model capability on real data. The results show that our model outperforms the state of the art, while reducing its computational footprint. Nevertheless, further research is needed to bridge the gap with real world applications.<br /

A generalised model of electrical energy demand from small household appliances

Accurate forecasting of residential energy loads is highly influenced by the use of electrical appliances, which not only affect electrical energy use but also internal heat gains, which in turn affects thermal energy use. It is therefore important to accurately understand the characteristics of appliance use and to embed this understanding into predictive models to support load forecast and building design decisions. Bottom-up techniques that account for the variability in socio-demographic characteristics of the occupants and their behaviour patterns constitute a powerful tool to this end, and are potentially able to inform the design of Demand Side Management strategies in homes. To this end, this paper presents a comparison of alternative strategies to stochastically model the temporal energy use of low-load appliances (meaning those whose annual energy share is individually small but significant when considered as a group). In particular, discrete-time Markov processes and survival analysis have been explored. Rigorous mathematical procedures, including cluster analysis, have been employed to identify a parsimonious strategy for the modelling of variations in energy demand over time of the four principle categories of small appliances: audio-visual, computing, kitchen and other small appliances. From this it is concluded that a model of the duration for which appliances survive in discrete states expressed as bins in fraction of maximum power demand performs best. This general solution may be integrated with relative ease with dynamic simulation programs, to complement existing models of relatively large load appliances for the comprehensive simulation of household appliance use

Integrated modelling of electrical energy systems for the study of residential demand response strategies

Building and urban energy simulation software aim to model the energy flows in buildings and urban communities in which most of them are located, providing tools that assist in the decision-making process to improve their initial and ongoing energy performance. To maintain their utility, they must continually develop in tandem with emerging technologies in the energy field. Demand Response (DR) strategies represent one such family of technology that has been identified as a key and affordable solution in the global transition towards clean energy generation and use, in particular at the residential scale. This thesis contributes towards the development and application of a comprehensive building and urban energy simulation capability that parsimoniously represents occupants' energy using behaviours and responses to strategies to influence them. This platform intends to better unify the modelling of Demand Response strategies, by integrating the modelling of different energy systems through Multi Agent Simulation, considering stochastic processes taking place in electricity demand and supply. This is addressed by: (a) improving the fidelity of predictions of household electricity demand, using stochastic models, (b) demonstrating the potential of Demand Response strategies using Multi-Agent Simulation and machine learning techniques, (c) integrating a suitable model for the low voltage network to study and incorporate effects on the grid, (d) identifying how this platform should be extended to better represent human-to-device interactions; to test strategies designed to influence the scope and timing of occupants' energy using services. Stochastic demand models provide the means to realistically simulate power demands, which are subject to naturally random human behaviour. In this work, the power demand arising from small household appliances is identified as a stochastic variable, for which different candidate modelling methods are explored. Variants of two types of stochastic models have been tested, based on discrete time and continuous time stochastic processes. The alternative candidate models are compared and validated using Household Electricity Survey data, which is also used to test strategies, informed by advanced cluster analysis techniques, to simplify the form of these models. The recommended small appliance model is integrated with a Multi Agent Simulation (MAS) platform, which is in turn extended and deployed to test DR strategies, such as load shifting and electric storage operation. In the search for optimal load-shifting strategies, machine learning algorithms, Q-learning in particular, are utilised. The application of this new developed tool, No-MASS/DR, is demonstrated through the study of strategies to maximise the locally generated renewable energy of a single household and a small community of buildings connected to a Low Voltage network. Finally, an explicit model of the Low Voltage (LV) network has been developed and coupled with the DR framework. The model solves for power-flow analysis of a general low-voltage distribution network, using an electrical circuit-based approach, implemented as a novel recursive algorithm, that can efficiently calculate the voltages at different nodes of a complex branched network. The work accomplished in this thesis contributes to the understanding of residential electricity management, by developing better unified modelling of Demand Response strategies, that require integrated modelling of energy systems, with a particular focus on the study of maximising locally generated renewable energy

Integrated modelling of electrical energy systems for the study of residential demand response strategies

Building and urban energy simulation software aim to model the energy flows in buildings and urban communities in which most of them are located, providing tools that assist in the decision-making process to improve their initial and ongoing energy performance. To maintain their utility, they must continually develop in tandem with emerging technologies in the energy field. Demand Response (DR) strategies represent one such family of technology that has been identified as a key and affordable solution in the global transition towards clean energy generation and use, in particular at the residential scale. This thesis contributes towards the development and application of a comprehensive building and urban energy simulation capability that parsimoniously represents occupants' energy using behaviours and responses to strategies to influence them. This platform intends to better unify the modelling of Demand Response strategies, by integrating the modelling of different energy systems through Multi Agent Simulation, considering stochastic processes taking place in electricity demand and supply. This is addressed by: (a) improving the fidelity of predictions of household electricity demand, using stochastic models, (b) demonstrating the potential of Demand Response strategies using Multi-Agent Simulation and machine learning techniques, (c) integrating a suitable model for the low voltage network to study and incorporate effects on the grid, (d) identifying how this platform should be extended to better represent human-to-device interactions; to test strategies designed to influence the scope and timing of occupants' energy using services. Stochastic demand models provide the means to realistically simulate power demands, which are subject to naturally random human behaviour. In this work, the power demand arising from small household appliances is identified as a stochastic variable, for which different candidate modelling methods are explored. Variants of two types of stochastic models have been tested, based on discrete time and continuous time stochastic processes. The alternative candidate models are compared and validated using Household Electricity Survey data, which is also used to test strategies, informed by advanced cluster analysis techniques, to simplify the form of these models. The recommended small appliance model is integrated with a Multi Agent Simulation (MAS) platform, which is in turn extended and deployed to test DR strategies, such as load shifting and electric storage operation. In the search for optimal load-shifting strategies, machine learning algorithms, Q-learning in particular, are utilised. The application of this new developed tool, No-MASS/DR, is demonstrated through the study of strategies to maximise the locally generated renewable energy of a single household and a small community of buildings connected to a Low Voltage network. Finally, an explicit model of the Low Voltage (LV) network has been developed and coupled with the DR framework. The model solves for power-flow analysis of a general low-voltage distribution network, using an electrical circuit-based approach, implemented as a novel recursive algorithm, that can efficiently calculate the voltages at different nodes of a complex branched network. The work accomplished in this thesis contributes to the understanding of residential electricity management, by developing better unified modelling of Demand Response strategies, that require integrated modelling of energy systems, with a particular focus on the study of maximising locally generated renewable energy

Measuring self-esteem in Spanish adolescents: Equivalence across gender and educational levels

Rosenberg's self-esteem scale (RSES) has been applied in many areas of psychology, highlighting the interest in the study of gender differences and educational level. At the same time, there was a methodological debate on its psychometric properties. Evidence points at a scale measuring a single trait confounded by a method factor associated to negatively worded items. The aim of the study is to examine RSES differences due to gender and educational level at the factor level, while controlling for the presence of method effects, in Spanish students. A completely a priori model was separately tested in four subsamples: college men and women, and high school men and women, and an invariance routine implemented for them. The primary conclusions are that the scale measures equally well in the four samples, and there were no latent mean differences due to gender or educational level. La escala de autoestima de Rosenberg (RSES) se ha empleado en muchas áreas de la psicología, destacando el interés por el estudio de las diferencias de género y de nivel educativo. Paralelamente, ha habido un debate metodológico sobre sus propiedades psicométricas: la evidencia señala que mide un solo rasgo de autoestima, pero confundido con un efecto de método asociado a los ítems invertidos. El objetivo de este estudio es examinar las diferencias en género y nivel educativo de la RSES, controlando por la presencia de efectos de método en estudiantes españoles. Se estimó un modelo completamente a priori en cuatro muestras: estudiantes hombres y mujeres de instituto y de universidad, y se implementó una rutina completa de invarianza factorial. Los principales resultados son que la escala mide de forma adecuada a las cuatro muestras y no hubo diferencias en las medias latentes en función del género o el nivel educativo

El rol mediador de l'envelliment actiu sobre la satisfacció i la salut percebudes: Un model estructural en majors angolesos

El benestar en persones majors és un constructe que ha protagonitzat moltes investigacions al llarg de les últimes dècades. Com ja és sabut, el benestar es troba a la base d¿una sèrie d¿indicadors. L¿objectiu d¿aquest estudi és valorar la possible mediació exercida per indicadors del nivell d¿activitat i independència funcional de les persones majors, en la relació d¿un nombre d¿indicadors sociodemogràfics i la satisfacció vital i la salut percebuda. Es fa ús d¿una mostra de 1.003 persones majors de Luanda (Angola), pertanyents a residències, diverses ONG i centres d¿atenció. S¿ha estimat un model d¿equacions estructurals que disposava d¿una sèrie de variables sociodemogràfiques que predeien la satisfacció vital i la salut dels majors, amb dos indicadors d¿activitat i dependència com a variables intervinents o mediadores. Els resultats concorden amb la bibliografia i es troba una forta implicació dels indicadors sociodemogràfics i de les variables relacionades amb l¿activitat/dependència, en la satisfacció vital i la percepció de salut de les persones majors

Socio-Demographic Variables and Successful Aging of the Angolan Elderly

The proportion of elderly people is growing faster than any other age group. Amongst them, the group of oldest old is indeed the segment of the elderly population with the fastest growth rate. The increase in the proportion of elderly in the Angolan population makes research on this area badly needed. Within the theoretical framework of successful aging, the study aims to test for sociodemographic group differences in perceived health, life satisfaction, and social relations in Angolan elderly. The dependent variables are three of the components of what has been called successful aging. Data came from a cross-sectional survey of elderly people living in Luanda. 1003 Angolan elderly were surveyed on sociodemographic information, perceived health, life satisfaction, and social support. MANOVAs were calculated to test for mean differences in the dependent variables. Results permit to conclude that the factors associated with the largest differences on the Angolan elderly’s quality of life and social relations were age (becoming oldest old) and institutionalization. The interactions of several factors with age pointed out that the oldest old were clearly a group in which the decreased quality of life due to becoming oldest old could not be compensated by other factors, as it was the case in the group of young old