Abstract
This study explores models for synthetic data generation of time series. In order to improve the achieved results, i.e., the data generated, new ways of improvement are explored and different models of synthetic data generation are compared. The model addressed in this work is the Generative Adversarial Networks (GANs), known for generating data similar to the original basis data through the training of a generator. The GANs are applied using the datasets of Quinta de Santa Bárbara and the Pinhão region, with the main variables being the Average temperature, Wind direction, Average wind speed, Maximum instantaneous wind speed and Solar radiation. The model allowed to generate missing data in a given period and, in turn, enables to analyze the results and compare them with those of a multiple linear regression method, being able to evaluate the effectiveness of the generated data. In this way, through the study and analysis of the GANs we can see if the model presents effectiveness and accuracy in the synthetic generation of meteorological data. With the proper conclusions of the results, this information can be used in order to improve the search for different models and the ability to generate synthetic time series data, which is representative of the real, original, data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This paper presents an interactive game designed to improve users’ experience related to driving behaviour, as well as to provide decision support in this context. This paper explores machine learning (ML) methods to enhance the decision-making and automation in a gaming environment. It examines various ML strategies, including supervised, unsupervised, and Reinforcement Learning (RL), emphasizing RL’s effectiveness in interactive environments and its combination with Deep Learning, culminating in Deep Reinforcement Learning (DRL) for intricate decision-making processes. By leveraging these concepts, a practical application considering a gaming scenario is presented, which replicates vehicle behaviour simulations from real-world driving scenarios. Ultimately, the objective of this research is to contribute to the ML and artificial intelligence (AI) fields by introducing methods that could transform the way player agents adapt and interact with the environment and other agents decisions, leading to more authentic and fluid gaming experiences. Additionally, by considering recreational and serious games as case studies, this work aims to demonstrate the versatility of these methods, providing a rich, dynamic environment for testing the adaptability and responsiveness, while can also offer a context for applying these advancements to simulate and solve real-world problems in the complex and dynamic domain of mobility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Artificial Intelligence (AI) is now ubiquitous in daily life, significantly impacting society by supporting decision-making. However, in many application areas, understanding the rationale behind AI decisions is crucial, highlighting the need for explainable AI (XAI). AI algorithms often lack transparency, making it hard to understand their inner workings. This work presents an overview of XAI solutions for decision support in mobility context. It addresses the complexity of explaining decision support models by offering explanations in various formats tailored to different user profiles. By integrating language models, XAI models may generate texts with varying technical detail levels, aiding ethical AI deployment and bridging the gap between complex models and human interpretability. This work explores the need for flexible explanation formats, supporting varied user profiles with graphical, textual, and tabular explanations. By integrating natural language processing models personalized explanations that are accurate, understandable, and accessible to a diverse audience can be generated. This study ultimately aims to support the task of making XAI robust and user-friendly, boosting its widespread use and application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The adoption of residential photovoltaic (PV) systems to mitigate the effects of climate change has been incentivized in recent years by government policies. Due to the impacts of these systems on the energy mix and the electrical grid, it is essential to understand how these technologies will expand in urban areas. To fulfill that need, this article presents an innovative method for modeling the diffusion of residential PV systems in urban environments that employs spatial analysis and urban characteristics to identify residences at the subarea level with the potential for installing PV systems, along with temporal analysis to project the adoption growth of these systems over time. This approach integrates urban characteristics such as population density, socioeconomic data, public environmental awareness, rooftop space availability, and population interest in new technologies. Results for the diffusion of PV systems in a Brazilian city are compared with real adoption data. The results are presented in thematic maps showing the spatiotemporal distribution of potential adopters of PV systems. This information is essential for creating efficient decarbonization plans because, while many households can afford these systems, interest in new technologies and knowledge of the benefits of clean energy are also necessary for their adoption.

Abstract
The effectiveness of optimizing complex problems is closely linked to the configuration of parameters in search algorithms, especially when considering metaheuristic optimization models. Although various automated methods for algorithm configuration have been proposed to alleviate users from manually tuning parameters, there is still unexplored potential in dynamically adjusting certain algorithm parameters during execution, which can lead to enhanced performance. The main objective is to comparatively analyze the effectiveness of manual parameter tuning compared to a dynamic online configuration approach based on reinforcement learning. To this end, the State-Action-Reward-State-Action (SARSA) algorithm is adapted to adjust the parameters of a genetic algorithm, namely population size, crossover rate, mutation rate, and number of generations. Tests are conducted with these two methods on benchmark functions commonly used in the literature. Additionally, the proposed model has been evaluated in a practical problem of optimizing energy trading portfolios in the electricity market. Results indicate that the reinforcement learning-based algorithm tends to achieve seemingly better results than manual configuration, while maintaining very similar execution times. This result suggests that online parameter tuning approaches may be more effective and offer a viable alternative for optimization in metaheuristic algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Currently, energy consumption in residential buildings is increasingly high. To meet demand, renewable energies are increasingly being used to produce more energy in a sustainable way, which has led to an increase in the load on the distribution network. Thus, with the exponential growth of dependence on technologies, studies on consumption patterns are increasingly common in order to try to understand the needs of the population and, in this way, make a more rational and efficient use of energy. This article aims to find consumption patterns in residential devices, considering specific houses. This work proposes the use of the Apriori algorithm, which allows the creation of several association rules among devices. The results, considering several scenarios in a house with 9 appliances, show that, despite the Apriori algorithm's difficulty in finding associations in household appliances with little time of use, several interesting association rules can be identified, providing relevant insights for future consumption flexibility models applications.