Portfolio optimization in the energy market

Let’s put ourselves in the shoes of an energy company. Our fleet of electricity production plants mainly includes gas, hydroelectric and waste-to-energy plants. We also sold contracts for the supply of gas and electricity. For each year we have to plan the trading of the volumes needed by the plants and customers: better to fix the price of these volumes in advance with the so-called forward contracts, instead of waiting for the delivery months, exposing ourselves to price uncertainty. Here’s the thing: trying to keep uncertainty under control in a market that has never shown such extreme scenarios as in recent years: a pandemic, a worsening climate crisis and a war that is affecting economies around the world have made the energy market more volatile than ever. How to make decisions in such uncertain contexts? There is an optimization problem: given a year, we need to choose the optimal planning of volume trading times, to meet the needs of our portfolio at the best prices, taking into account the liquidity constraints given by the market and the risk constraints imposed by the company. Algorithms are needed for the generation of market scenarios over a finite time horizon, that is, a probabilistic distribution that allows a view of all the dates between now and the end of the year of interest. Algorithms are needed to solve the optimization problem: we have proposed more than one and compared them; a very simple one, which avoids considering part of the complexity, moving on to a scenario approach and finally a reinforcement learning approach.

How to explain: from theory to practice

Today we live in an age where the internet and artificial intelligence allow us to search for information through impressive amounts of data, opening up revolutionary new ways to make sense of reality and understand our world. However, it is still an area of improvement to exploit the full potential of large amounts of explainable information by distilling it automatically in an intuitive and user-centred explanation. For instance, different people (or artificial agents) may search for and request different types of information in a different order, so it is unlikely that a short explanation can suffice for all needs in the most generic case. Moreover, dumping a large portion of explainable information in a one-size-fits-all representation may also be sub-optimal, as the needed information may be scarce and dispersed across hundreds of pages. The aim of this work is to investigate how to automatically generate (user-centred) explanations from heterogeneous and large collections of data, with a focus on the concept of explanation in a broad sense, as a critical artefact for intelligence, regardless of whether it is human or robotic. Our approach builds on and extends Achinstein’s philosophical theory of explanations, where explaining is an illocutionary (i.e., broad but relevant) act of usefully answering questions. Specifically, we provide the theoretical foundations of Explanatory Artificial Intelligence (YAI), formally defining a user-centred explanatory tool and the space of all possible explanations, or explanatory space, generated by it. We present empirical results in support of our theory, showcasing the implementation of YAI tools and strategies for assessing explainability. To justify and evaluate the proposed theories and models, we considered case studies at the intersection of artificial intelligence and law, particularly European legislation. Our tools helped produce better explanations of software documentation and legal texts for humans and complex regulations for reinforcement learning agents.

6G enabled IoV scenarios with distributed intelligence mechanisms

The Internet of Vehicles (IoV) paradigm has emerged in recent times, where with the support of technologies like the Internet of Things and V2X , Vehicular Users (VUs) can access different services through internet connectivity. With the support of 6G technology, the IoV paradigm will evolve further and converge into a fully connected and intelligent vehicular system. However, this brings new challenges over dynamic and resource-constrained vehicular systems, and advanced solutions are demanded. This dissertation analyzes the future 6G enabled IoV systems demands, corresponding challenges, and provides various solutions to address them. The vehicular services and application requests demands proper data processing solutions with the support of distributed computing environments such as Vehicular Edge Computing (VEC). While analyzing the performance of VEC systems it is important to take into account the limited resources, coverage, and vehicular mobility into account. Recently, Non terrestrial Networks (NTN) have gained huge popularity for boosting the coverage and capacity of terrestrial wireless networks. Integrating such NTN facilities into the terrestrial VEC system can address the above mentioned challenges. Additionally, such integrated Terrestrial and Non-terrestrial networks (T-NTN) can also be considered to provide advanced intelligent solutions with the support of the edge intelligence paradigm. In this dissertation, we proposed an edge computing-enabled joint T-NTN-based vehicular system architecture to serve VUs. Next, we analyze the terrestrial VEC systems performance for VUs data processing problems and propose solutions to improve the performance in terms of latency and energy costs. Next, we extend the scenario toward the joint T-NTN system and address the problem of distributed data processing through ML-based solutions. We also proposed advanced distributed learning frameworks with the support of a joint T-NTN framework with edge computing facilities. In the end, proper conclusive remarks and several future directions are provided for the proposed solutions.

Evaluating the Impact of Recommender Systems to Society

Nowadays, Recommender systems play a key role in managing information overload, particularly in areas such as e-commerce, music and cinema. However, despite their good-natured goal, in recent years there has been a growing awareness of their involvement in creating unwanted effects on society, such as creating biases of popularity or filter bubble. This thesis is an attempt to investigate the role of RS and its stakeholders in creating such effects. A simulation study will be performed using EcoAgent, an RL-based multi-stakeholder recommendation system, in a simulation environment that captures key user interactions, suppliers and the recommender system in order to identify possible unhealthy scenarios for stakeholders. In particular, we focus on analyzing the document catalog to see how the diversity of topics that users have access to varies during interactions. Finally, some post-processing methods will be defined on EcoAgent, one reactive and one proactive, which allows us to manipulate the agent’s behavior in order to study whether and how the topic distribution of documents is affected by content providers and by the fairness of the system.

Analysing the base of precast column in socket foundations with smooth interfaces

This paper analyzes the behavior of the base of a precast column in the socket foundation with smooth interfaces. This research is motivated by the lack of information and guidelines on the behavior of column bases in the embedded region. An experimental program with two full-scale specimens was carried-out. These two specimens had smooth interfaces at the internal faces of the socket, different embedded lengths and were subjected to loads with large eccentricities. The experimental results showed that the failure of the specimens occurred by the yielding of the longitudinal reinforcement out of the embedded region, while the transverse reinforcement was not very stressed. Some recommendations on the anchorage of the longitudinal reinforcement and a strut-and-tie model for the behavior of column bases in the embedded region are proposed.

Falls prevention in rural general practice: what stands the test of time and where to from here?

Objective: General practitioner recall of the 1992-96 'Stay on Your Feet'(SOYF) program and its influence on practice were surveyed five years post-intervention to gauge sustainability of the SOYF General Practice (GP) component. Methods: A survey assessed which SOYF components were still in existence, current practice related to falls prevention, and interest in professional development. All general practitioners (GPs) situated within the boundaries of a rural Area Health Service were mailed a survey in late 2001. Results: Response rate was 66.5% (139/ 209). Of 117 GPs in practice at the time of SOYF, 80.2% reported having heard of SOYF and 74.4% of those felt it had influenced practice. Half (50.9%) still had a copy of the SOYF GP resource and of those, 58.6% used it at least 'occasionally'. Three-quarters of GPs surveyed (75.2%) checked medications 'most/almost all' of the time with patients over 60 years; 46.7% assessed falls risk factors; 41.3% gave advice; and 22.6% referred to allied health practitioners. GPs indicated a strong interest in falls prevention- related professional development. There was no significant association between use of the SOYF resource package and any of the current falls prevention practices (all chi(2)>0.05). Conclusions and implications: There was high recall of SOYF and a general belief that it influenced practice. There was little indication that use of the resource had any lasting influence on GPs' practices. In future, careful thought needs to go into designing a program that has potential to affect long-term change in GPs' falls prevention practice.

Regional investment strategies (RIS) and coastal catchment initiative (CCI) project alignmnet to Great Barrier Reef Water Quality Protection Plan

The Great Barrier Reef Water Quality Protection Plan (the Reef Plan) is a joint initiative of the Australian and Queensland Governments. The Reef Plan aims to progress an integrated approach to natural resource management planning by building on the existing partnerships between the different levels of government, industry groups, the community and research providers within the Reef catchments, principally through partnerships with the regional natural resource management (NRM) bodies.

Estudo do efeito da face sobre o equilíbrio de estruturas de contenção de solo reforçado sob condições de trabalho

Os métodos de análise de estruturas de contenção de solo reforçado sob condições de trabalho, em geral, desconsideram a contribuição da face para o equilíbrio da estrutura. Visando estudar a influência do peso específico da face e das propriedades relacionadas à rigidez da mesma sobre o desempenho das estruturas de solo reforçado, são realizadas simulações numéricas de diversas estruturas, utilizando a versão de dupla precisão do programa CRISP92-SC. Avalia-se, também, o emprego de diferentes tipos de elementos para a representação da face. Verifica-se que a face rígida impõe redução significativa das solicitações máximas de tração nos reforços e dos deslocamentos das estruturas de solo reforçado. A influência do peso específico da face sobre a estabilidade interna dos maciços reforçados mostrase desprezível e constata-se que a rigidez à flexão e a rigidez axial da face, função da sua geometria e do seu módulo de Young, são parâmetros influentes no comportamento das estruturas de contenção de solo reforçado. As variações da tração no reforço e da resultante de força cortante na face, em decorrência do enrijecimento da face, são analisadas e propõe-se uma relação entre elas. Quanto à forma de representação de uma face com rigidez expressiva, na simulação de uma estrutura de solo reforçado com o CRISP92-SC, é observado que a representação da face, seja por elementos de viga, seja por elementos quadriláteros, não altera os resultados da análise.

Multiagent system for adaptive strategy formulation in electricity markets

Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal.

Análise de risco em sequenciamento de actividades com restrições de recursos

Mestrado em Engenharia Electrotécnica e de Computadores. Área de Especialização em Sistemas e Planeamento Industrial.

Mix design process of polyester polymer mortars modified with recycled GFRP waste materials

In this study, the effect of incorporation of recycled glass fibre reinforced plastics (GFRP) waste materials, obtained by means of shredding and milling processes, on mechanical behaviour of polyester polymer mortars (PM) was assessed. For this purpose, different contents of GFRP recyclates, between 4% up to 12% in weight, were incorporated into polyester PM materials as sand aggregates and filler replacements. The effect of the addition of a silane coupling agent to resin binder was also evaluated. Applied waste material was proceeding from the shredding of the leftovers resultant from the cutting and assembly processes of GFRP pultrusion profiles. Currently, these leftovers as well as non-conform products and scrap resulting from pultrusion manufacturing process are landfilled, with additional costs to producers and suppliers. Hence, besides the evident environmental benefits, a viable and feasible solution for these wastes would also conduct to significant economic advantages. Design of experiments and data treatment were accomplish by means of full factorial design approach and analysis of variance ANOVA. Experimental results were promising toward the recyclability of GFRP waste materials as partial replacement of aggregates and reinforcement for PM materials, with significant improvements on mechanical properties of resultant mortars with regards to waste-free formulations.

Integração de reacção e deliberação em agentes inteligentes

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Redes de Comunicação e Multimédia

Viabilidade do reforço sísmico de um edifício de pequeno porte em alvenaria de pedra ordinária

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Estruturas

LNT: a logical neighbor tree secure group communication scheme for wireless sensor networks

Secure group communication is a paradigm that primarily designates one-to-many communication security. The proposed works relevant to secure group communication have predominantly considered the whole network as being a single group managed by a central powerful node capable of supporting heavy communication, computation and storage cost. However, a typical Wireless Sensor Network (WSN) may contain several groups, and each one is maintained by a sensor node (the group controller) with constrained resources. Moreover, the previously proposed schemes require a multicast routing support to deliver the rekeying messages. Nevertheless, multicast routing can incur heavy storage and communication overheads in the case of a wireless sensor network. Due to these two major limitations, we have reckoned it necessary to propose a new secure group communication with a lightweight rekeying process. Our proposal overcomes the two limitations mentioned above, and can be applied to a homogeneous WSN with resource-constrained nodes with no need for a multicast routing support. Actually, the analysis and simulation results have clearly demonstrated that our scheme outperforms the previous well-known solutions.

Design of experiments applied to the mix design process of polymer mortar materials modified with GFRP recyclates

In this work, the effect of incorporation of recycled glass fibre reinforced plastics (GFRP) waste materials, obtained by means of shredding and milling processes, on mechanical behavior of polyester polymer mortar (PM) materials was assessed. For this purpose, different contents of GFRP recyclates (between 4% up to 12% in mass), were incorporated into polyester PM materials as sand aggregates and filler replacements. The effect of silane coupling agent addition to resin binder was also evaluated. Applied waste material was proceeding from the shredding of the leftovers resultant from the cutting and assembly processes of GFRP pultrusion profiles. Currently, these leftovers, jointly with unfinished products and scrap resulting from pultrusion manufacturing process, are landfilled, with supplementary added costs. Thus, besides the evident environmental benefits, a viable and feasible solution for these wastes would also conduct to significant economic advantages. Design of experiments and data treatment were accomplish by means of full factorial design approach and analysis of variance ANOVA. Experimental results were promising toward the recyclability of GFRP waste materials as aggregates and reinforcement for PM materials, with significant improvements on mechanical properties with regard to non-modified formulations.