Abstract
This paper presents a methodology based on genetic Algorithms (GA) to solve the problem of optimal participation in multiple electricity markets. With the emergence of new requirements for electrical power markets, it has become fundamental to develop tools to aid in decision making, understanding the functioning of markets and forecast iterations that occur between the different entities in the market. Artificial intelligence plays a crucial role in the development of these tools. Using artificial intelligence techniques, it is possible to simulate the different existing players in the market, to enable these players to be adaptive to any situation, and to model any type of trading. Artificial intelligence based metaheuristic optimization tools allow solving problems in a short time, and with very close results to those that deterministic techniques are able to achieve, at the cost of a high execution time. The achieved results, using a simulation scenario based on real data from the Iberian electricity market, show that the proposed method is able to reach better results than previous implementations of a Particle Swarm Optimization (PSO) and a Simulated Annealing (SA) methods, while achieving very similar objective function results to those of a deterministic approach, in a much faster execution time.

Abstract
This paper proposes a novel case-based reasoning (CBR) approach to support the intelligent management of energy resources in a residential context. The proposed approach analyzes previous cases of consumption reduction in houses, and determines the amount that should be reduced in each moment and in each context, in order to meet the users' needs in terms of comfort while minimizing the energy bill. The actual energy resources management is executed using the SCADA House Intelligent Management (SHIM) system, which schedules the use of the different resources, taking into account the suggested reduction amount. A case study is presented, using data from Brazilian consumers. Several scenarios are considered, representing different combinations concerning the type of house/inhabitants, the season, the type of used energy tariff, the use of Photovoltaic system (PV) generation, and the maximum amount of allowed reduction. Results show that the proposed CBR approach is able to suggest appropriate amounts of energy reduction, which result in significant reductions of the energy bill, while, with the use of SHIM, minimizing the reduction of users' comfort.

Abstract
The increasing penetration of renewable generation brings a significant escalation of intermittency to the power and energy system. This variability requires a new degree of flexibility from the whole system. The active participation of small and medium players becomes essential in this context. This is only possible by using adequate forecasting techniques applied both to the consumption and to generation. However, the large number of incontrollable factors, such as the presence of consumers in the building, the luminosity, or external temperature, makes the forecasting of energy consumption an arduous task. This paper addresses the electrical energy consumption forecasting problem, by studying the correlation between the solar radiation and the electrical consumption of lights. This study is performed by means of three forecasting methods, namely a multi-layer perceptron artificial neural network, a support vector regression method, and a linear regression method. The performed studies are analyzed using data gathered from a real installation - campus of the Polytechnic of Porto, in real time.

Abstract
Artificial Intelligence (AI) methods contribute to the construction of systems where there is a need to automate the tasks. They are typically used for problems that have a large response time, or when a mathematical method cannot be used to solve the problem. However, the application of AI brings an added complexity to the development of such applications. AI has been frequently applied in the power systems field, namely in Electricity Markets (EM). In this area, AI applications are essentially used to forecast /estimate the prices of electricity or to search for the best opportunity to sell the product. This paper proposes a clustering methodology that is combined with fuzzy logic in order to perform the estimation of EM prices. The proposed method is based on the application of a clustering methodology that groups historic energy contracts according to their prices' similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts' history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts.

Abstract
Electricity markets worldwide are complex and dynamic environments with very particular characteristics, resulting from their restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. The rising complexity and unpredictability in electricity markets has increased the need for the intervenient entities in foreseeing market behaviour. Several modelling tools directed to the study of restructured wholesale electricity markets have emerged. However, they have a common limitation: the lack of interoperability between the various systems to allow the exchange of information and knowledge, to test different market models and to allow market players from different systems to interact in common market environments. This paper proposes the Electricity Markets Ontology, which integrates the essential necessary concepts related with electricity markets, while enabling an easier cooperation and adequate communication between related systems. Additionally, it can be extended and complemented according to the needs of other simulators and real systems in this area.

Abstract
The Arrowhead Framework is a European effort that aims to apply Service Oriented Architecture to the embedded systems' world. The Event Handler system is a component that supports the handling of events, and in that sense it enriches service-oriented applications with the capabilities of interacting via the publish/subscribe paradigm. In fact, the Event Handler system is in charge of the notification of events that occur in a given Arrowhead compliant installation, manages producers and consumers of events, allows filtering of messages, and manages historical data regarding events. This latter capability is performed either on local files, on a database, or through another component of the Arrowhead Framework - the Historian system. The net result of the integration of the Event Handler in an Arrowhead Framework simplifies and empowers the communication of its components, as it is demonstrated in the paper with two examples: the management of application faults, and the support to quality of service of orchestrated services.