Renato Silva Fernandes

Abstract
In this paper, for the data set of the Iberian Electricity Market for the period 1 January 2015 to 30 June 2019, 19 different models are considered from econometrics, statistics, and artificial intelligence to explain how electricity markets work. This survey allows us to obtain a more complete, critical view of the most cited models. The machine learning models appear to be very good at selecting the best explanatory variables for the price. They provide an interesting insight into how much the price depends on each variable under a nonlinear perspective. Notwithstanding, it might be necessary to make the results understandable. Both the autoregressive models and the linear regression models can provide clear explanations for each explanatory variable, with special attention given to GARCHX and LASSO regression, which provide a cleaner linear result by removing variables that have a minimal linear impact.

Abstract
This paper analyzes the electricity prices of the MIBEL electricity spot market with respect to a set of possible explanatory variables. Understanding the main drivers of the electricity price is a key aspect in understanding price formation and in developing forecasting models, which are essential for the selling and buying strategies of market agents. For this analysis, different techniques have been applied in this work, including standard and lasso regression models, causal analysis based on bayesian networks and classification trees. Results from the different approaches are coherent and show strong dependency of the electricity prices with the Portuguese imported coal for lower non-dispatchable net demands, which has been progressively replaced by gas for larger non-dispatchable net demands. Hydro reservoirs and hydro production are also main explanatory variables of the electricity price for all non-dispatchable net demand levels.

Abstract
In order to improve their competitive performance, airline companies often adopt as a strategy to establish arrangement between two or more organizations agreeing to cooperate on a substantial level. This strategy is often known as airline alliances. A paradigm to analyze the collective intelligence behavior which emerges from a group, as a strategic alliance, is the flocking behavior. Inspired by the Cucker and Smale algorithm (C-S) we propose a new version of the flocking behavior algorithm applied to airline alliances. Our goal is to understand the link between strategic alliances and flocks. For this new approach, metrics were obtained for the parameters of C-S algorithm, namely position, velocity and influence, where the latter uses cooperative games. Besides, reinforcement learning mechanisms have been explored. Some relevant outputs for airline alliances as the permanence rate and the growth rate were computed for each of the five configurations in analysis.

Abstract
Simulation is often used to forecast human populations. In this paper we use a novel approach by combining Micro-Macro (MicMac) models into an Agent-Based perspective to simulate and forecast the behavior of the Portuguese population. The models include migrations and three scenarios corresponding to three different expected economic growth rates. We conclude that the increase in the number of emigrants leads to a reduction of the Portuguese women that are in the fertile age. This justifies the decrease of births and therefore the general decrease of the total Portuguese Population.